Like explorers embarking on a quest for the unknown, businesses, including startups, are venturing into a new era powered by artificial intelligence and machine learning. AI is transforming businesses and companies, irrespective of niche, and taking them on an exhilarating journey of unprecedented growth and success.

However, when you think about artificial intelligence and its business application, your mind may jump to automation. After all, it is the first thing that pops into most people’s heads. Although some applications and uses cases of AI do involve automating business processes and procedures that were originally managed or executed by humans, this phenomenon only scratches the surface of what AI, deep learning, and machine learning can do.

Not only is artificial intelligence helping people become more productive and efficient, it is also revolutionizing how we do business. Did you know that 86% of CEOs say that AI is now a mainstay in their offices? You will be surprised to learn that it is not in the form of futuristic robots or complex machinery, but rather software to run their daily operations.

From predicting consumer behavior and buying habits to lowering manual data entry, artificial intelligence in business has become indispensable in ways never imagined before.

In business, AI has a wide range of applications and uses. Actually, most people interact with AI technology in some form or another daily. From dull and menial to breathtaking, artificial intelligence and related technologies are already disrupting virtually every business process in almost every industry.

For example, the MD Anderson Cancer Center’s ambitious “Moonshot” project harnessed IBM’s Watson to transform cancer treatment. Just as the popular cancer center embraced AI in different capacities, executives, entrepreneurs, and business owners are gearing up to embrace this valuable and revolutionary technology.

As various AI technologies proliferate, they are quickly becoming imperative to maintain a competitive edge. Let’s explore how this tech innovation is reshaping and transforming businesses, one step at a time.

What is Artificial Intelligence?

Before we examine how AI technologies, such as deep learning, are impacting the modern business world, it is vital to define the term. “Artificial intelligence” is a branch of computer science and a broad term that refers to any kind of computer software or program that engages in human-like activities, such as learning, planning, and even problem-solving.

However, note that calling specific applications “AI” is like calling a car a “vehicle” – while it is technically correct, it does not cover any of the specifics. So, it is important to understand the details.

Artificial Intelligence aims to create machines and systems, such as predictive analysis and forecasting systems, which can perform tasks that typically require intuition or human intelligence. It entails developing computer programs and algorithms that enable robots or machines to learn from experience, and adapt to new inputs. That is fascinating, right?

Machines and programs are also trained to perform tasks with human-like intelligence, like problem-solving, natural language understanding, decision-making, speech recognition, and image recognition.

AI systems are powerful and can analyze large datasets and recognize patterns. They can also make predictions or recommendations on the basis of the data they have learned from. AI algorithms meticulously sift through colossal amounts of data, sorting out hidden patterns and nuggets of wisdom. This is an impressive feat!

Whether it is predicting market trends or understanding customer preferences and habits, AI turns raw data into powerful and actionable insights, empowering decision-makers to make strategic and informed choices.

Companies like Amazon and Etsy use AI and machine learning to analyze customer behavior, optimize pricing, and recommend products. On the other hand, Netflix wields AI to suggest binge-worthy shows and movies, keeping viewers glued to their screens like die-hard fans cheering for their favorite team!

The Proliferation of Artificial Intelligence in Business 

Companies and businesses today have access to more data than ever before. Forbes revealed that the amount of data generated and consumed increased by a whopping 5000% from 2010 to 2020. With the help of various emerging technologies and tech marvels, companies in all industries are now able to capture valuable user data that can help them make reliable and informed business decisions. 

Also, in the last few years, many organizations and companies have wholeheartedly welcomed and embraced AI to make more effective use of the data they already collect. For instance, consider the following numbers: 

• According to Grand View Research, the global AI market size was worth an impressive $62 billion in 2020. However, it is expected to have a growth rate (annual) of more than 40% from 2021 to 2028.
• Accenture’s recent report on AI revealed that almost 84% of C-suite executives, such as CEOs and CTOs, think leveraging AI and similar tools will help them meet their growth objectives. 
• ⦁ 56% of survey respondents in McKinsey’s “The state of AI in 2021” survey mention adopting AI.

These numbers and statistics show that AI and machine learning are no longer experimental technologies only used by large companies or select brands. For most companies worldwide, it has now become a core part of their business operations.

AI and Modern Business Enterprises

Rather than replacing human intelligence, intuition, and ingenuity, AI is usually seen as a supporting and valuable tool. AI is adept at processing and analyzing troves of data, such as customer information, much faster than a human brain ever could. AI software and tools can then suggest synthesized courses of action to human users.

This is how we can use AI and similar tools to help game out possible consequences or ramifications of each action, streamlining the decision-making process.

However, it is prudent to embrace AI in practical ways, such as automating simple tasks or using a piecemeal approach. This is how it can yield remarkable results. Much like NASA’s RPA pilots that started with simple tasks, businesses in different niches and sectors can leverage AI to automate mundane processes, leading to quick returns on investment.

By unleashing AI’s magic on data, companies gain powerful insights, understand customers better, and make strategic decisions like seasoned champions on the field.

Common Uses and Applications of AI

Some standard uses and applications of AI are cybersecurity, personalization, customer relationship management, and personal assistants.

Personalization – Tailoring Experiences Like a Bespoke Suit

In the corporate world, personalized experiences are like a well-tailored bespoke suit, fitting each customer perfectly. Leveraging AI for cognitive insight allows companies to analyze vast data volumes, providing detailed and precise patterns. Just like athletes strive for better performance with each game, machine learning models and AI algorithms continuously improve.

By understanding customer behavior, companies can offer personalized recommendations and targeted marketing. As Vanguard’s success with “Personal Advisor Services” shows, integrating AI with human expertise in customer interactions creates a winning combination, leading to high customer satisfaction and financial gains.

Cybersecurity – Proactive Recognition and Detection

AI is an indispensable ally, especially when looking for holes or flaws in computer network defenses. For instance, AI systems and tools can recognize a cyberattack and other cyber threats. They do this by monitoring unique patterns from data input.

Once these systems detect a threat, they can backtrack through the data in order to find the source and help avert a future threat. That extra pair of eyes – one that’s as continuous and diligent as AI – will serve an excellent role in preserving your tech infrastructure.

Providing Product Recommendations  

Companies can also use AI to recommend products and services that will align with customers’ needs and interests and keep them engaged.

For example, by tracking and monitoring customer behavior on your site, you can easily present your customers with products or services that are similar to the ones they have already viewed or used. Keep in mind that this is an especially valuable tactic for companies in the competitive e-commerce space. 

AI-Driven Insights – Illuminating the Path to Success

AI is the Sherlock Holmes of the modern business world, uncovering hidden trends and clues that competitors often overlook. By analyzing vast datasets, AI tools reveal valuable insights, helping companies make data-driven and informed decisions.

For example, AI helps financial institutions, such as banks and credit unions, detect fraudulent activities by identifying unusual patterns.

Final Thoughts

As the curtain rises on the AI-powered business revolution, companies of all sizes must grab the spotlight and take center stage. Rather than replacing human intelligence and ingenuity, AI can complement human efforts.

As businesses launch pilot projects and gradually scale up, they navigate the AI revolution like intrepid explorers venturing into uncharted territories. With a blend of AI-powered insights, automation, personalization, and top-notch customer service, companies can conquer new frontiers and emerge victorious champions in the ever-evolving business arena.

Sources

https://professional.dce.harvard.edu/programs/artificial-intelligence-in-business-creating-value-with-machine-learning/

https://hbr.org/2018/01/artificial-intelligence-for-the-real-world

https://www.forbes.com/sites/forbesbusinesscouncil/2022/11/21/the-top-five-ways-ai-is-transforming-business/

https://www.mckinsey.com/capabilities/quantumblack/our-insights/generative-ai-is-here-how-tools-like-chatgpt-could-change-your-business

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AI and BI have long-term impacts on their respective fields, but what will the impact be? Before heading into more detail, it’s necessary to brush a few basics and find the most viable impact of these technologies on the future of work.

AI and BI: The Basics

When we look at the new technology available to humans, Artificial Intelligence (AI) is the most common in the modern era. AI revolves around using computerized systems to create human intelligence-like operations. It can include tasks like:

⦁ Problem-solving
⦁ Learning
⦁ And judgment

Although the tech is still in its early stages, businesses have long focused on its applications and long-term impacts. According to reports, 33% to 44% of companies use AI technology in their business operations. This number has increased further in recent years with the improvement of AI systems available.

Business Intelligence (BI) consists of all the tools and options for analyzing and collecting business data. Since data plays a vital role in business, deploying BI solutions can greatly improve operations.

BI primarily provides this information to enterprises to improve their decision-making abilities. The right decisions can put the business on the right trajectory, ensuring its long-term growth and development.

Although BI is one of the most useful options for long-term business growth, knowing its applications matters. Since BI is a newer concept, seeking training for it is not a bad idea.

All business professionals should substantially understand BI and data analysis processes operations. With the help of an advanced business degree in business analytics prepares these professionals for the required data aptitudes.

Although the primary aims of AI and BI differ, they have overlapping applications for various enterprises. However, the differences are much more varied than the overlapping elements. This article will overview the impact of AI and BI on the future of work. By understanding the applications of the two technologies, businesses can deploy the two tools better.

How AI and BI will change the Future of Work

According to the reports by World Economic Forum, Artificial Intelligence (AI) will replace around 85 million jobs by 2025. While that sounds negative, the report also suggests that AI technology will create 97 million new jobs. McKinsey, on the other hand, believes that nearly half of the business leaders have one business function with AI in it.

Great data visualizations are possible with the help of Business Intelligence. However, how the users interpret these visualizations is up to them. Various Artificial Intelligence plugins have made creating quick and efficient summaries of these visualizations easier.

With the collaboration of the two technologies, it’s simpler to derive easy-to-understand narratives, allowing users to create informed decisions. AI can help enrich the existing Business Intelligence models in the following ways:

.

⦁ Advanced and Sophisticated Analysis
The Business Intelligence options allow users to visualize, organize, and analyze information and data for the enterprise. These tools offer higher efficiency, taking a step further than basic tools like spreadsheets.

However, deriving the results from the data is still a manual process requiring certain technical experience. Therefore, the insight depends on individual perceptions. The AI-powered BI system pulls the possible information from a larger data set. It can include extraneous data.

AI uses some of the oldest available data to forecast new data for the required pointers. It also clears out the vitality of each data point at a minute level. Artificial Intelligence also helps business owners determine a data set and its translations in business decisions.

⦁ Informed Decision Making
AI is the quickest way to extract business-related insights from existing business models. According to PwC research, 54% of executives believe AI improves decision-making. For instance, sales and marketing are two common examples where using AI-based decisions can help. AI uses the data from BI and draws certain forecasts about future customer behaviors from it.

AI provides predictive analytics, which is the primary benefit of the integration of the technology in BI. With the help of predictive analysis, creating a roadmap and setting business metrics becomes easier. They also help use various statistical algorithms for real-time information analysis. Adopting the best course of action becomes simpler and easy with all of this information.

⦁ Proactive Decisions
Using BI to extract historical data and focus on past events rather than the future adds another dimension to business processes. With the right amalgamation of the two technologies, getting data-driven and improved decisions is simple.

AI analytics ensures all decisions made are based on data backing. With the help of this tool, you can also ensure unique choices and better results. Merging BI and AI analytics helps create proactive decisions, preventing poor results. For instance, A healthcare facility can use BI to determine the re-admission rates of patients.

With AI’s help, determining the patients with higher risks of re-admissions also becomes easier. The facility can then use the information from the two technologies, taking proactive steps to reduce this percentage.

The use of the AI insights received also helps with trend prediction. It focuses on optimizing logistics operations and setting the right pricing for the services offered. Professionals working in the particular domain can make relevant changes accordingly.

The perceived market demand is the key player in this case. The professionals can also deliver the products without an order confirmation, like Amazon does, improving the overall business process.

Final Thoughts
The use of AI and BI in business has greatly improved the overall functionality and scope of business. Merging the two technologies provides a more holistic approach to business decision-making.

BI focuses more on the existing insights and draws possible predictions from them. AI, on the other hand, helps understand the insights from BI for better analysis and decision-making.

Although AI is still nascent, its long-term applications will improve the business landscape. Business professionals are readily deploying AI and BI together in business structures, and it does show results.

Technology Options
IDA is the most up-to-date interactive AI/BI data analytics platform, and its no-programming design enables users to ask/request any unstructured question or request on their data without the lengthy delays typically found in development through conventional BI systems, unlocking unprecedented speed to knowledge. Using IDA, you and your team can boost productivity and make better business decisions.

REFERENCES

https://treehousetechgroup.com/how-ai-is-impacting-business-analytics-insights/

https://adinga.co.za/impact-of-artificial-intelligence-on-business-and-society/

https://suyati.com/blog/the-benefits-of-adding-ai-to-business-intelligence/

https://planergy.com/blog/impact-of-ai-on-business/

<p>The post How AI and BI Will Change the Future of Work first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

The demand for efficient, sophisticated, and better AI tools is increasing as we are stepping into a new phase of the world. It’s fair to say this demand will only increase in the future as most businesses, organizations, and processes are now powered by AI.

However, large systems, energy, and complicated computation are required for running and maintaining these AI systems. With so many resources used in AI tools and systems, several concerns regarding the AI system’s carbon footprint are a major consideration.

One of the most popular remedies to this carbon footprint is Green AI. However, only a few individuals know what it is and why its matters. It’s necessary to brush on some basics before diving into why it matters.

Green AI: What is it?
Green AI simply means sustainable, environment-friendly artificial intelligence. The Green AI introduces sustainability within this system with efficient systems. It also focuses on developing and adopting AI models requiring lesser computational costs, therefore, a lower carbon footprint.

Since AI is becoming inseparable in today’s world, it’s wise to focus on methods to make it efficient. Institutions, industries, and AI enthusiasts all support the concept of Green AI, so we can use artificial intelligence without endangering the environment.

Why does Green AI matter?
Although Green AI is no longer a new concept, people haven’t fully understood why it’s necessary.

Detailed research on the computational energy required for advanced AI training shows alarming results. It claims nearly 250,000 pounds of carbon dioxide is needed to train a single AI system. Multiply the carbon footprint for the total number of AI systems required, and you’ll understand how big of a deal it is.

If AI continues its popularity the way it is right now, it can have horrendous impacts on the environment. So, what can we do to minimize these issues? — Adopt Green AI for a safer future tomorrow.

We need better, more efficient, and sustainable AI models to meet the current demand without wasting too much energy. With contained carbon emissions, saving the environment without compromising on using AI is possible.

Benefits of Adopting Green AI
Green AI has shown tremendous potential for a better future. It offers several environmental benefits, allowing AI enthusiasts to utilize the tech without causing environmental harm. Here’s a breakdown of the top benefits of adopting Green AI.

⦁ Control Energy Usage

Energy conversion is the first step to a sustainable tomorrow. We see many key players in the industry deploying these methods until we have better alternatives for AI usage.

A classic example is Google’s DeepMind AI, utilizing wind farms for better energy production. With DeepMind’s machine learning algorithms, Google develops accurate estimations of the output from these farms.

They even claim to have a 20% increase in power production because of using DeepMind AI within their wind farms. They have everything scheduled out for maximum energy output, allowing better results in the grid than other energy sources.

⦁ Improve Product Development and Eliminate Waste Pollution

The latest advancements in AI allow the business to use technology for monitoring and improving current energy schemes. With Green AI, you can streamline products for better, sustainable, and highly responsive systems. Once the companies adopt these changes on a larger scale, decarbonizing their AI use is pretty straightforward.

Green AI has also become pretty famous on the governmental level, as many nations are officially investing resources. One example is the United Kingdom, according to the UK’s officials, with Green AI resolving UK’s Grand Challenges is possible. These challenges include four transformative global trends within the UK market and energy industry.

The government is making major efforts to improve system efficiency. A major step in these efforts includes £200 million of funding towards a thousand new AI PhDs so that more people step into the industry in the next five years. With more experts, finding better solutions is a matter of time.

Another notable change is the AI Sector Deal published back in April 2018. The Deal included UK government partnerships for AI systems, such as the Open Data Institute. These changes have provided better options like waste reduction strategies.

As these systems grow more sophisticated, we anticipate seeing more global governments view points on Green AI more seriously.

⦁ Regenerate Natural Resources

Regenerative agriculture is becoming a popular concept due to its environmental and productivity perks. By deploying the right AI tech, access to more data for guidance, revitalizing environments, and better strategizing is possible.

Multiple new approaches like precision farming also have plentiful potential. It can help unlock strategies allowing the farmers to optimize and improve the soil quality. These farmers enjoy a higher yield without damaging the environment, allowing them to obtain better results.

AI has already become a major part of regenerative and precision agriculture in different areas worldwide.

For instance, Canada’s Precision AI has a weed-killing drone (which runs on AI), allowing farmers to detect and spray weeds, saving crops from dying. It’s a practical and affordable approach for better crop maintenance and effective week-killer deployment.

⦁ Close The Loop

When you look at the world of recycling, you will come across the term “close the loop.” Experts are doing plenty of efforts to control wastage and increase recycling opportunities. However, there’s still a huge gap.

It’s possible to recycle several metals like aluminum, requiring lesser new raw material. The closed looped recycle is the optimal technique because it increases the resource efficiency required to control production. Many supermarkets have one kind of recycled plastic from these systems, allowing maximum results.

With AI and other tech within the manufacturer, one can better perform the following tasks:

⦁ Track
⦁ Aggregate
⦁ Close The Loop On For Items Like Recycled Plastic

⦁ Best Allocation Of Renewable Energy

Renewable energy is viable for mass application if one can do it properly. Ironically, there’s a huge amount of energy waste even in the green energy sector. These sectors produce extra energy that doesn’t get used up anywhere.

However, with the use of Green AI in these processes, we can hope for better and more efficient results.

Final Thoughts

Green AI is a popular and effective approach for a safer, sustainable future. However, we still need a lot of work in the industry and much to learn before we can deploy Green AI on a mass level. It has promising applications in various industries. Many AI enthusiasts have already utilized it in their businesses, and with a little improvising, we might see it used everywhere on a more larger scale in the coming years.

<p>The post The Importance of Green AI for a better tomorrow? first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

IDC, the market intelligence leader, predicts that global data will increase ten times by 2025. As a result, there will be more data-driven enterprises with data literate workforce. The fact of the matter is that the business landscape has become more aggressively competitive and saturated.

The need of the hour for companies is to create a data culture to follow the tide of success and achieve long-term growth. Data has become integral for companies to attain and retain their competitive edge in the market. It has become a valuable strategic asset for businesses to transform their operations.

Data literacy also empowers the workforce and leads to professional development. The higher the data literacy, the better results companies can expect. But not every company is able to improve its data literacy. In the age of digital transformation, you’d be surprised how many companies are left behind.

Let’s dive into the fundamentals of data literacy and what makes it essential for businesses in 2023:

Data Literacy Basics

Data literacy is all about your ability to read, write, and communicate data in a specific context. Employees and data scientists have to review the data critically and understand its contextual relevance to make actionable steps.

Business insights are nothing but an ocean of quantitative and qualitative data. Strategic and calculated business decision-making comes into play when you make sense of data. And employee data literacy involves understanding and dissecting data insights to help company leaders optimize results.

In 2023, it has become crucial for companies of all sizes and operating in different industries to make data more accessible and expand their use cases. And contrary to naïve misconception, data literacy extends beyond data analysts – it applies to employees and company leaders.

Data Literacy and Company’s Growth: Connect the Dots!

If the company’s workforce is data literate, it is bound to make informed decisions and discover new opportunities. In fact, how companies collect and review data can impact their future growth and ability to make future business decisions.

State of Data Literacy

Data literacy continues to have newfound meaning for companies in 2023. While analytics and big data solutions crossed the $200 million threshold last year, many companies still lack data literacy to make growth-driven business decisions.

A Gartner survey highlights that over 50% of companies lack AI skills and data literacy to improve business operations and maintain the growth rate. In the end, it’s not about how much data a company collects – it’s about how it extracts useful data to make informed business decisions. Remember, analytical data revolves around actionable insights and creates added value for businesses.

Another Gartner report shows that employees can resolve most problems by addressing data deficiencies. Data literacy can become one of the main hurdles or assets for companies. Data literacy strategically pushes companies and improves their ability to grow over time.

What Data Literacy Can Do for Companies

Data literacy determines how fast the company will drive growth. It can also determine whether or not the company will be able to sustain growth for a long time. The workforce can harness the power of data to positively impact daily activities.

Data literacy helps company leaders and the workforce see the big picture. With the right approach at the right time, data literacy helps companies boost performance and streamline operations. At this stage, you won’t rely on data scientists to help you make sense of every data element and minimize bottlenecks.

After all, a data-literate workforce knows how to handle complex data and categorize elements that might lead to informed decisions for their roles and departments from the bottom up.

How to Build and Improve Data Literacy

Boosting data literacy doesn’t happen overnight – it takes consistent and gradual efforts to make a data-centric organizational culture. You may have noticed that strategic business advisors often put emphasis on data and help companies create data literacy workshops and programs. It is the best approach to boost data literacy.

Here are some steps you can take to improve your company’s data literacy:

• Establish your company’s existing data literacy and take initiatives to improve how your workforce collects, stores, accesses, and reviews data.
• Spot data inconsistencies and gaps that might create issues in the foreseeable future.
• Collaborate with data analytics to help the rest of the employees become data literate.
• Identify main communication hurdles and ensure data is used for shared and specific purposes.
• Create a data-first organizational culture and support ideas.
• Ensure data accessibility is within limits to ensure there is no manipulation or illegal data sharing.
• Raise awareness about data literacy and touch on “why” and “how” it can help the company drive growth. Roll out training programs to make employees more data literate.
• Use data visualization tools to contextualize different elements of data.
• Ensure your data literacy activities, programs, and training sessions are engaging.
• Instead of generalizing data literacy activities for everyone, focus on how specific employees can tap into data literacy opportunities and improve their collective understanding.
• Ensure the development and launch of various products or services are supported by data analytics.
• Company leaders should lead by example and demonstrate practical expertise in data management and utilization to improve daily business decision-making.

Wrapping Up

There is a paradigm shift in data utilization and creating a data-centric company culture. Organizations are at the forefront of finding new ways of storing, organizing, managing, and analyzing data. It has become “the” factor to stand out in the market. Progressive business leaders now understand that data investing increases the value of their assets. Since data itself has become an asset, data literacy geared toward making informed business decisions has become a top priority for business leaders.

REFERENCES:

• https://www.qlik.com/us/bi/data-literacy
• https://www.dataiq.global/articles/articles/the-importance-of-data-literacy-in-the-workplace
• https://www.networkworld.com/article/3325397/idc-expect-175-zettabytes-of-data-worldwide-by-2025.html
• https://www.idc.com/getdoc.jsp?containerId=prUS48165721
• https://www.forbes.com/sites/gilpress/2017/01/20/6-predictions-for-the-203-billion-big-data-analytics-market/?sh=72ea39302083
• https://www.gartner.com/smarterwithgartner/a-data-and-analytics-leaders-guide-to-data-literacy
• https://www.precisely.com/blog/data-integrity/data-literacy-what-it-is-and-why-it-matters
• https://www.techrepublic.com/article/what-is-data-literacy/
• https://www.linkedin.com/pulse/importance-data-literacy-tiffany-perkins-munn-ph-d-?trk=public_profile_article_view

<p>The post Importance of Data Literacy for Enterprises in 2023 first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

But to move forward, decision-makers have to leverage the best aspects of data analytics to assess losses. Business leaders also have to be at the forefront to support the adoption of data analytics and deploy various data analytics tools and solutions to cut back on losses.

Remote Setting and Personalized Approach to Assess Loss Using Data Analytics

It becomes trickier for business leaders to assess loss using data analytics in remote settings. On the surface, measuring growth may seem straightforward. But each company has to pull its own bag, understand its strengths and weaknesses, and maintain a competitive market position.

Data analytics specialists assess the process to quantify the overall loss and then create personalized plans to recover losses. The proactive participation of company stakeholders is essential to ensure the successful use and deployment of data analytics.

Right after delivery, you can determine the short-term and long-term impact and track changes to get ready for more potential losses. Part of the process is to focus on delivery systems and make data analytics use cases more inclusive and constructive. And that means consistently improving understanding of data analytics to make intelligent decisions.

How to Assess Losses Using Data Analytics

With solid data analytics tools, you can take faster and better actions and avoid losses. But to assess losses, you have to go back to data analytics uses. Teams can use data analytics for many things. In the context of assessing the loss, teams can specifically use data analytics in three ways.

Operational Data Analytics
You can use automated systems that source multiple data points and algorithms to get desired results and find out elements that trigger internal organizational responses. Take a closer look at your data dashboards and interpret the authenticity of results and how you can make significant improvements.

Strategic Data Analytics
In this type of assessment, you review various data sources to help management make strategic business decisions. Strategic analytics is about extracting data from multiple business systems and reviewing and comparing their efficacy.

Tactical Data Analytics
Teams can use assessed data streams to address particular issues more efficiently. Focus on investigating anomalies like refunded frauds, out-of-stock options, and above-average rates. This kind of analytics will help you extract information from business data systems and help investigators find key reasons behind loss.

Best Ways to Utilize Data Analytics to Minimize Risk of Losses.

Here are the most practical approaches for CFOs and business leaders to integrate data analytics across risk and loss assessment processes:

Identifying Losses and Potential Risk of Losses
When it comes to identifying loss and potential risk of losses, the first step is to understand the undue influence of external and internal elements. Focus on elements that restrict a company’s finances and limit its ability to run smooth operations. Don’t forget compliance objectives if there’s a direct rendered loss.

Again, to address emerging risks and potential losses, consider external and internal factors. Whether you realize it or not, data analytics has the power to improve and accelerate the quality of risk assessments. In most cases, assessing losses and potential risks is an annual or bi-annual event.

The solution is centralizing vital loss and risk assessment data inputs and historical results. Leverage business intelligence solutions and tools to shed light on losses and make periodic changes to avoid similar impacts in the foreseeable future.

Assessing Losses and Risk of Potential Losses
Once you identify losses and risks of potential losses across transactional levels, determine the likelihood of those losses to occur again and impact the entire organization. Data analytics comes to the rescue and can help you understand the precise impact and impact probability with simple measurements

Pair analyzed data with internal sources and perform an audit to get findings, turnover rate, operational loss, and impact on financial performance. Next, companies can corroborate impact assessments and the likelihood of the loss and recommend strategic decisions that mitigate such potential losses.

Lastly, track external data sources and analyze valuable insights to review financial loss and risk. The most cost-effective and robust technique is to improve your quantitative risk and loss measures. Consistently keep an eye on regulators and unstructured data that might trigger another operational loss for the company.

Monitoring Response to Losses and Risks of Potential Losses
Once you move past the probabilities and significant impact, create a response mechanism to mitigate the loss. Ensure your response is highly effective to mitigate the overall loss and risk of the potential loss. Tap into data analytics to track quantitative metrics.

It is of utmost importance to maintain consistency to maintain optimal risk level management and achieve maturity across all strategic assets of the company. Take a position on whether or not the current loss and risk management standards are enough to scale up operations, generate more revenue, and drive growth. Indirect risks can also increase the potential risk of loss for the company.

So, ensure a vibrant organizational workplace culture and eliminate cybersecurity loopholes. While no company can completely avoid losses, integrating data analytics makes it easier to understand critical elements that lead to losses. Data analytics can help you perform comprehensive risk identification, risk monitoring, and risk assessment and create solid loss prevention responses.

Final Thoughts
Companies should regularly review and improve their risk assessment processes using data analytics. Focus on external and tech factors when reviewing loss using data analytics. It is the best way to understand operational complexities that lead to loss.

In the grand scheme of things, companies should have a custom and comprehensive risk management framework to assess and mitigate losses. It will also help enterprises check the risk and loss assessment process and create room for more effectiveness.

REFERENCES:
⦁ https://www.datapine.com/blog/data-analysis-methods-and-techniques/
⦁ https://www.stitchdata.com/resources/benefits-of-data-analytics/
⦁ https://www.tandfonline.com/doi/full/10.1080/01605682.2022.2041373
⦁ https://www.ft.com/partnercontent/netskope/evaluating-the-cost-of-data-loss.html
⦁ https://www.helastel.com/how-data-analytics-can-help-a-business/

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Today, you can use a wide range of tools and solutions to forecast your business tasks and streamline the workflow. The key is to review forecasted performance using forecasting methods and data analytics. Ultimately, it has become crucial for businesses to implement and evaluate their own business forecasting.

Business forecasting ties together with predictive analytics. While predictive analytics is still relatively new, it can transform business forecasting. Business forecasting paired with analytics is a perfect combination, allowing companies to take advantage of big data and drive unprecedented growth.

A comprehensive report shows that the business analytics market is set to grow at 7.8% for the next 5 years. What’s interesting is that increased digitalization has paved the way for the business analytics market to thrive and evolve. In fact, business forecasting via analytics continues to help businesses form a clear understanding of their customers.

What Constitutes Business Forecasting?

Business forecasting involves a combination of techniques and tools to analytically predict business development. With business forecasting, you can shed light on sales, profits, and expenditures. The foundational core of business forecasting is to help companies develop and implement more effective strategies to make smart decisions.

You collect and analyze historical data and run it through qualitative and quantitative models to spot patterns that predict the effectiveness of future production, marketing operations, and financial operations.

Business Forecasting: Role of Big Data and Analytics

Big data acts as a growth driver and helps companies anticipate many foreseeable business opportunities and obstacles. Contrary to misguided perception, analytics doesn’t have to be complex. It is a matter of having robust models to extract the most accurate business insights and make strategic, calculated, and logical decisions.

You can perform forecasting to paint a clear picture of your business in 2023, and big data and analytics are at the center. Specifically, data analytics allows you to make sense of a multitude of elements and centralize information to make decision-making more effective.

In the digital and tech-driven age, organizations collect a lot of data from customers, employees, suppliers, and transactional processes. But often, companies don’t realize that analytics has the power to simplify big data and ensure accurate decision-making.

Process of Business Forecasting

Step #1
Identify relevant variables and figure out the best way to collect datasets.

Step #2
Identify the data points related to a problem and perform a systematic investigation.

Step #3
Predict future business operations using collected information through systematic investigation.

Step #4
Select a model that works well with the dataset, estimates, and variables. Use the selected model to perform data analysis and make forecasts.  

Step #5
Consider possible deviations between the forecast and the actual performance.

Step #6
Using this information, refine business processes to anticipate and improve the accuracy of forecasts in the foreseeable future.

Business Analytics and Its Benefits

• Business analytics refers to statistical analysis application that uses technologies to predict business outcomes and anticipate trends.
• When analytics comes into the picture, you can use different analysis models to better understand realities, predict future scenarios, and even create new scenarios.
• Business analytics revolves around operational analysis, data visualization, and quantitative analysis. Your goal should be to use gained insights to improve the business decision-making process.
• One of the hallmark perks of business analytics is that it improves your operational efficiency and helps you better understand your future outcomes and customers.
• It also helps you measure performance, support strategic decision-making, discover new trends, scale up business operations, generate more leads, and drive long-term business growth.

Don’t Confuse Business Analytics with Data Analytics

Business analytics is part of data analytics. And companies now use data analytics for different purposes to find patterns and trends to address specific problems. Data analytics uses data transformation, data cleansing, and data modeling to help companies find these patterns and trends.

Companies need to use essential business analytics methods. Primarily, business analytics comes down to predictive modeling, statistical analysis, and data mining to drive focused and accurate business decisions.

Predictive analytics, descriptive analytics, and prescriptive analytics are the main business analytics types. There is also diagnostic analytics that utilizes analytics to unearth new reasons and factors for current or past performance.

How to Get Started with Business Forecasting

First, get familiar with key business forecasting approaches. Second, find out which method is most suitable for your business. Instead of generalizing, opt for the forecasting technique that caters to your business needs.

Focus on your data and what you’ve achieved in the past to dive into business forecasting. Business forecasting is a matter of asking the right questions. After that, use these answers to navigate your business decisions.

Once you establish a data point or a problem that deserves the focus of attention, take into account available historical data to create an accurate prediction. You can perform business forecasting using qualitative methods if you’re a new startup.

Next, ask yourself how far you intend to forecast. The farther you decide to forecast, the more market changes you will have to consider. Ideally, opt for short-term business forecasting to focus on the most accurate quantitative methods and current company data.

While the timeframe of short-term business forecasting is three months, long-term business forecasting can be for more than a year. It also reveals more pattern and trend changes and requires more expertise to analyze data using qualitative methods and demographics.

Final Thoughts

With the integration of modern technologies within business analytics, there is bound to be more growth in the market. While business forecasting is far from perfect, analytics solutions allow businesses to understand their company’s financial situation and maintain an agile position in the ever-evolving and competitive market. In retrospect, business forecasting propels you to get to know your company and collect valuable insights to make smarter and more informed business decisions.

• https://www.investopedia.com/articles/financial-theory/11/basics-business-forcasting.asp
• https://hbr.org/1971/07/how-to-choose-the-right-forecasting-technique
• https://www.upwork.com/resources/business-forecasting
• https://www.projectmanager.com/blog/business-forecasting
• https://www.wrike.com/blog/what-is-business-forecasting/
• https://learn.g2.com/business-forecasting
• https://www.businessnewsdaily.com/16007-financial-forecasting.html

<p>The post Forecast Your 2023 Business Outlook Using Analytics first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

In 2022, growth-driven public sector entities at the local government level are adopting data analytics to improve their management plans. Several local government organizations have started to harness the power of data analytics to make informed and calculated decisions. It is an innovative approach to decision-making that allows local governments to transform their cities and counties.

Specifically, city governments can use performance analytics, which involves using statistical tools and techniques to find, communicate, and understand new data patterns. But these practices have to work in tandem to improve operational efficiency, eliminate redundancies, and reduce costs.  

In the last few years, there has been a rise in the adoption of advanced data analytics solutions that provides local governments a chance to reshape their cities and counties. Fundamentally, data analytics allows local governments to address the most common issues of their cities and counties.

Using data analytics, local governments can integrate data from different departments into customizable and centralized operations. City and county governments can leverage discovery tools and reports to break down data silos. Local governments can also use the same tools to improve citizen engagement and facilitate local leaders to serve their people better.

Moving Towards Advanced Data Analytics

In 2022, county and city governments across the nation can use data analytics to help municipalities improve data-driven strategies. Despite their existing analytics capabilities, city and county governments should work closely with municipalities to modernize data analytics solutions and create a roadmap to move forward in the right direction.

Broad Perspective

A survey highlights that around 90% of parks and recreation departments consider using data analytics to improve daily operational decision-making and focus on more data-driven strategic efforts. But it will take some before every public sector company starts using data analytics to collect accurate data and make informed decisions.

For city and county governments, challenges can creep in easily and complicate operations. But the promise and potential of data analytics can help local governments realize short-term and long-term benefits. Many counties in cities are already transitioning to data analytics-based systems to track a variety of projects. It gives local governments a clear view to check the status of projects and realize benefits for citizens.

Ways City and County Governments Can Use Data Analytics

⦁ Citizen Engagement
Robust data analytics can help city and county governments step up their citizen engagement efforts. Data analytics solutions provide consistent opportunities to local governments to increase transparency, provide added support, and build trust with their citizens. For instance, city and county governments can use data analytics to boost constituent satisfaction, decrease incoming complaints, and clarify complicated processes in the form of visual content.

⦁ Public Safety
Local government service systems are limited when it comes to safety and security. With data analytics, city and county governments can focus on a holistic view of people’s safety. It also allows local law enforcement agencies to review data and determine neighborhoods at high risk of crime. Data analytics can help local governments take proactive cross-departmental steps. When local leaders can analyze data on a granular level, they’re bound to discover more trends across the city.

⦁ Emergency Response
With data analytics and predictive analytics modeling, city and county governments can improve their emergency response in severe weather conditions. Data analytics can help local leaders make informed and swift decisions before a weather event can impact people.
Analytics solutions can help cities and counties review collected data about implemented emergency responses and address complaints without delay. In an unattended local neighborhood, local leaders can create a measured and calculated emergency response. It is the best approach for local leaders to check the effectiveness of their emergency response.

⦁ Public Finance & Administration
Typically, local governments face a lot of pressure to provide reasoning behind their expenditures. Public sector agencies can improve their operational efficiency, customer service, and reduce costs by pairing operations with data analytics systems.

Consequently, it would allow city and county governments to optimize their budget and current financial systems. Local government can also benefit from workforce analytics to manage human capital. Similarly, data analytics can help city and county governments predict and prevent waste, abuse, and fraud.

⦁ Social and Public Health Services
Data analytics can help local governments transform their social and public health services. A data-driven approach allows public sector organizations to reduce healthcare expenses and improve the overall quality of care.

With data analytics, local governments can streamline resource allocation and service delivery. Data analytics can also help local governments anticipate and avoid opiate overdoses and ensure access to healthcare services.

⦁ Transportation
Data analytics systems help local government leaders get relevant insights to improve current and future transportation projects. For starters, city and county government officials can perform environmental analysis before finalizing and implementing construction plans.

Transportation management comes with a lot of nuances, and data analytics allows local leaders to measure the real-time impact of traffic on different projects. City government leaders can optimize and protect their infrastructure assets with data analytics. Data analytics can also help city and county officials to improve local transportation permitting procedures.

Conclusion

In line with data analytics, city and county governments can maintain a positive response and provide added perks to citizens. It is vital for city and county governments not to look for a one-size-fits-all solution. Like a typical organization, many departments fall under the umbrella of local government. The trick is to review the regulatory and cultural differences across departments and then create result-oriented strategies to improve city and county operations over time.

You can make an argument that local governments often underestimate the challenges that create more problems for people living in cities and counties. The objective of local government should be to foster a culture of learning and innovation. Whether it’s public recognition or monetary benefits, data analytics gives local governments more options to make citywide changes that can improve people’s lives.

REFERENCES:

https://avianaglobal.com/the-value-of-big-data-for-local-government/

https://www.americancityandcounty.com/2016/01/27/getting-the-most-from-data-analytics-projects-how-cities-can-handle-the-data-challenge/

https://expectexceptional.economist.com/impact-data-analytics-local-government.html

https://datasmart.ash.harvard.edu/news/article/analytics-city-government

<p>The post Transforming Cities and County Governments with Data Analytics Platforms first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

While there are several benefits of HR analytics, it depends on “how” companies utilize human resources data analytics. In a data-driven world, companies can heavily rely on HR analytics to better understand the effectiveness of their teams and optimize specific areas to drive growth.

What Constitutes HR Analytics?

HR analytics refers to direct data collection of employees and teams. The idea is to find new ways that can improve the effectiveness of the workforce. Accurately analyzing HR analytics data helps companies improve their existing functions.

For instance, HR managers can use analytics to collect data on individual performances. And all it takes is to monitor the total number of hired people in a month. It can help companies determine the ROI of each hired employee.

Measure the “Right” HR Metrics

It takes time to choose and track relevant HR metrics in a modern-day workplace environment. Whether it’s talent acquisition, outsourcing, or retention, focus on the meaningful data to measure the right HR metrics and achieve business goals. The most common HR metrics include cost per hire, ROI on each hire, retention rate, and time to hire. Ultimately, companies can use HR analytics powered by AI and automation to analyze key metrics flawlessly.

Benefits of HR Analytics

Here are a few essential perks of using HR analytics for companies:

Increase Productivity
KPIs are standard performance-based metrics companies can use to review ROI for each hire via HR analytics. It would also improve the bottom line of the company, identify the most underperforming employees, and offer prompt support.

Solid Talent Acquisition
Companies can use HR analytics to review areas like candidate experience, application completion, hire quality, source quality, and cost per hire to improve the acquisition process.

Maintain Low Turnover Rate
Most companies dread high turnover rates, and the Great Resignation made it more difficult to retain employees for a long time. Businesses can use HR analytics to focus on specific metrics that might force an employee to leave. Once companies have this information, they can increase their employee retention over time.

Improve Employee Satisfaction
From the HR perspective, employee and applicant experience plays a quintessential role in company growth. Companies can use HR analytics to make their onboarding process and recruitment efforts more effective. Once companies focus on retention strategies, it becomes easier to improve employee satisfaction. With HR analytics, companies can ensure applicants don’t encounter technical issues during the application or onboarding process.

Determine Skill Gaps
When it comes to automation and using analytics, up to 40%of entities are still in the early stages. Companies can use HR analytics to automate specific manual tasks and focus on different teams’ skill gaps. Part of the process of growing a solid workforce is to figure out who you should and shouldn’t hire. And HR analytics allows companies to make these decisions based on automation and data visualization.

Missed Trends and Patterns
Companies can use HR analytics powered by machine learning and AI to identify trends and patterns that ordinarily would’ve been missed. HR managers can feed this information to build management reports on the most overlooked areas.

Create Engaging Workforce
HR professionals concur that most employees directly engage in work when there are initiatives in place. Using HR analytics, companies can generate valuable insights about teams and better understand how to improve the organizational culture to increase employee engagement.

Professional Development and Training
Despite the position or industry, training is essential for professional development. In fact, helping employees learn a new set of skills and grow directly impacts the performance of the company. With HR analytics, you can gather data in areas where some employees may need upskilling. You can use this data to provide training opportunities and determine whether or not the training saved the company’s time and resources.

HR Analytics: What Should be the Approach of Your Company?
Companies can view talent management, performance monitoring, workforce management, and recruitment optimization through the lens of HR analytics. The goal of using HR analytics is to render actionable insights that can improve business operations. Ideally, opt for the most innovative and effective HR analytics tools.

Last Words

Investing resources and time into HR activities can be overwhelming. With HR analytics, companies can save valuable time and money and objectively review the performance of each hire with various metrics. Using HR analytics allows companies to be informed about HR departments and HR-related activities.

Effectively using HR analytics can help businesses increase overall profitability. HR professionals can also customize human resources data analytics to specific business needs. But remember that proper utilization of HR analytics depends on choosing the right metrics to improve a company’s ability to collect, review, and present findings.

Businesses understand that HR analytical tools are only effective when they collect and monitor useful data. The starting point for HR managers would be to review their existing processes to determine more suitable ways to measure success via HR analytics.

REFERENCES:

https://intellipaat.com/blog/hr-analytics/

https://www.aihr.com/blog/what-is-hr-analytics/

https://www.mckinsey.com/business-functions/people-and-organizational-performance/our-insights/how-to-be-great-at-people-analytics

https://home.kpmg/xx/en/home/insights/2018/11/hr-data-and-analytics.html

<p>The post How You Can Leverage HR Analytics for Your Company first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

It would be fair to state that the COVID-19 pandemic crisis changed the business landscape. In fact, companies had to scrap their long-term strategic plans when the economic outlook started to look grim. When a recession becomes a reality, the best course of action for businesses is to reset their plans. Business leaders understand that predicting how long the recession will last is difficult.

However, the fact remains that business cycles have to be run in a continuous momentum to maintain optimized operations and drive growth. Fortunately, companies can leverage a wide range of data analytics tools to navigate the economic recession. And using the right data analytics tools at the right time allows businesses to emerge stronger and make a swift recovery in the market.

Pair Recession Strategy with Data Analytics Tools

Having a recession strategy is integral for all businesses. But once you pair it with valuable data analytics tools, it allows you to achieve superior performance. In 2022, progressive companies using data analytics to navigate the challenges of recession are leading the charge. In fact, these firms have a pragmatic approach to utilizing data analytics tools and maintaining a strong market position.

Ideally, companies should use data analytics tools to cut costs and increase operational efficiency. Similarly, using data analytics tools helps companies get more confidence in their marketing, assets, and research and development efforts.

In addition, organizations should not feel reluctant to re-evaluate their core business models. Whether it’s supply chains, workflows, or core structure, companies can use data analytics tools to free more cash for other investments and improve efficiency. Both these elements ultimately help companies stimulate growth in the post-recession age.

At this stage, companies should adopt a progressive approach to make data analytics tools part of their recession strategy. Ordinarily, companies face different internal and external challenges that impact the ability of business leaders to make logical and calculated decisions. And this is where data analytics tools come to the rescue.

Navigating the Recession Using Data Analytics Tools

Companies can adapt a data-driven approach to make critical business decisions. Many companies have started to realize and recognize that data analytics tools serve as innovative tools. With data analytics tools, companies can review cost-cutting investment proposals and make growth-driven internal decisions.

In a broad sense, data analytics tools help companies make strategic steps and avoid falling behind during the recession. Not long ago, McKinsey recommended companies use advanced analytics tools and digital solutions to prepare defensive and offensive strategies for recession. Practically, companies should use next-gen data analytics tools to find customer-driven opportunities and maintain consistent growth amidst the recession.  

Recession and Using Data from Analytics Tools

Companies can avail services of a professional data analyst to better understand their internal data and connect the dots with the market trends. During the recession, the business dynamics and standards have changed for good. In fact, companies now have to focus on specific data points to make logical actions.

And data analysis allows companies to take a closer look at customers’ information, performance parameters, etc. In a crisis, companies can use collected data from analytics tools to analyze customer data, understand price limitations, review customers’ changing preferences, and effectively cut costs.  

Although it depends on the type of products or solutions a company offers in the market, it is better to use predictive analytics and data mining solutions to make critical business decisions. Using these models will allow companies to identify issues in specific areas, collect accurate information, prepare extensive data analysis reports, filter selected data, improve key indicators, and find patterns. Similarly, businesses could also visualize data in the form of charts and graphs to come to logical conclusions.

During the recession, business leaders are thrown into uncharted territory, and using data analytics tools makes it easier to navigate the storm and move forward in the right direction. Advanced data analytics tools help companies become more objective about their business condition.

With data analytics tools, businesses can accurately manage and predict supply and demand, adapt to new market changes, and decrease supplier risks. These changes positively impact the company and restore consumer confidence in the market. In an economic downturn, companies can use more than one data analytics tool to establish KPIs and navigate the market.

Most business leaders now understand that data analytics tools make it easier to measure KPIs. Businesses can use the data analytics tools to eliminate extraneous expenses, focus on current customer data, and support the sales department to increase profit projections.

Final Thoughts

Today, it has become clear that not many business leaders are prepared to deal with the recession. At its core, businesses have to balance out their financial position and brand management efforts. Whether it’s a large company with community shareholders or a small business, both are vulnerable to the short-term and long-term consequences of recessions.

In retrospect, companies have a unique opportunity to use a variety of data analytics tools and paint a clear picture of their business conditions. Data analytics solutions can also help businesses identify opportunities to improve performance and maintain optimal operations during a recession.

REFERENCES:

• https://www.techtarget.com/searchbusinessanalytics/news/252481362/Economic-downturn-shows-businesses-true-value-of-analytics
• https://www.stitchdata.com/resources/benefits-of-data-analytics/
• https://www.techtarget.com/searchbusinessanalytics/definition/big-data-analytics
• https://powerbi.microsoft.com/en-us/data-analytics-tools/
• https://www.pkfantares.com/how-data-analytics-can-help-busnisses/

<p>The post Navigating the Recession with Data Analytics Tools first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

The current market research predicts that data analytics in the education sector would cross the threshold of $57 billion by the end of 2030. In 2022, education institutions have become more aware of the lucrative benefits attached to data analytics. And more awareness means there is a new tide of a transformative learning system that would bring in more investments.

Data analytics can optimize administrative services and pave the way for more growth in the education sector. Since the COVID-19 pandemic crisis, the education sector has had its fair share of challenges, and data analytics can help the industry to move forward in the right direction.

As long as data analytics is an inclusive part of the education sector, it is bound to create a growth-driven environment and generate more revenue. Keeping that in mind, let’s touch on some of the best ways the education sector can leverage data analytics:

Use Cases of Data Analytics for the Education Sector

Review Performance of Students
The educator industry can leverage data analytics to create a holistic learning environment and ensure a heightened performance of students. The main objective of educational institutions should be to use data analytics to raise the bar of learning processes across all academic processes. Academic players can review the students’ records to gain more information and find out areas that need more improvement.

Data analytics solutions powered by AI and machine learning algorithms can help the education sector predict the most ideal career pathway as per the current performance of a student. Schools can also use data analytics to roll out more programs and improve testing initiatives to create a better learning environment for students.

Keep an Eye on Enrollment Trends
One of the ways the education sector can use data analytics is to monitor changing enrollment trends. For any school or college, this is the best way to attain students with the highest potential. Since parents have specific criteria when it comes to choosing an academic institute, it makes perfect sense to offer educational excellent in different aspects.

Educational institutions can monitor the enrollment of candidates and students for years to come. This approach would allow schools and colleges to retain the most talented students and decrease the drop-out rate in the foreseeable future. Schools and colleges can also review the visits on their official website and check traffic to understand the likes and dislikes of visitors.

Optimize Governance and Management
Prompt and uniform delivery of information is one of the major challenges for large educational institutions. With data analytics, however, universities can create custom programs to fill out the gaps and inconsistencies between parents, students, administration, and teachers.

Academic institutes can assign specific tasks through the cloud and then monitor the fulfillment of allocated resources. Data analytics paired with cloud computing can help schools to align their resources and efforts with essential academic requirements.

Ensure Digital Transformation
It is unfathomable for any business to survive let alone thrive without successful digital transformation. It is a dawn of a new digital era and data analytics is at the center of it. The educational industry can use data analytics solutions to centralize digital information, update social media accounts, and personalize websites on regular basis.

In fact, schools and colleges can use different channels to gain more understanding and form a unique perspective about students through data analytics solutions. Deploying smart data analytics solution would make it easier for educational institutions to find what they’re looking for in the first place.

Identify Most Effective Educational Practices
Schools and colleges can use data analytics to identify the most effective academic practices. The trick is to visualize and monitor the behavioral pattern of all students to find out the most successful initiatives and how they’ve had an impact on students in the past. This would propel schools and colleges to adopt more progressive measures and ensure academic success.

Adopt and Embrace New Tech Innovations
The use of advanced data analytics solutions would allow educational institutions to follow the tide of modern technologies. Data analytics is the culmination of statistics, qualitative information, and data metrics that can help schools to roll out more tech-driven initiatives.

On the other hand, schools and colleges can use collected data to find out the most cost-effective and newest software solutions to pave the way for more tech development. In fact, educational institutions can create a mobius circle and adopt new tech innovations without reluctance.

Evaluate Annual Academic Growth and Success Parameters
On top of supporting remote learning, schools and colleges can use data analytics to monitor the use of different devices, hardware, software, and other technologies throughout the day. After that, it would make it easier to review the outcomes and find out which variables deliver the best results.

Also, educators can use data analytics to analyze different ways students contextualize learning content. Furthermore, schools can use data analytics to build and encourage more competent learning where students are more comfortable to perform and achieve academic success.

Conduct Targeted Recruitment
With data analytics solutions, schools and colleges can also conduct targeted recruitment. This would allow schools and colleges to predict and analyze elements that would impact the application process. An in-depth and insightful knowledge would allow educational institutions to make certain modifications and recruit candidates that are highly suitable for their academic institutes.

Final Thoughts
Advanced data analytics solutions are more than capable to render the most valuable insights for educational institutions. In fact, these tools are like treasure maps for academic players to figure out different aspects of their operations that work and don’t work.

Data has become the Holy Grail for all industries and the education sector can use data analytics to optimize functions, support specific employees, better utilize resources, and fill out loopholes. Educational institutions can innovatively and efficiently harness the power of data analytics.

REFERENCES:

⦁ https://www.analyticsinsight.net/data-analytics-is-reshaping-education-industry-during-remote-learning/
⦁ https://theceoviews.com/how-is-data-analytics-transforming-the-education-industry/
⦁ https://www.linkedin.com/pulse/7-applications-data-analytics-education-reynaldo-junior-/
⦁ https://precisioncampus.com/blog/benefits-big-data-education/
⦁ https://www.nabler.com/higher-ed-solutions/articles/how-data-analytics-influences-education-sector/
⦁ https://www.globenewswire.com/news-release/2021/12/14/2351366/0/en/Big-Data-Analytics-in-Education-Market-Is-Expected-to-Reach-57-14-Billion-by-2030-Allied-Market-Research.html
⦁ https://www.othot.com/blog/2021-the-future-of-data-analytics-in-higher-education-is-prescriptive-analytics
⦁ https://www.lambdasolutions.net/blog/5-types-of-analytics-used-in-higher-education
⦁ https://inoxoft.com/impact-of-big-data-on-education-history-benefits-and-examples/
⦁ https://kitaboo.com/digitization-books/

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