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/

<p>The post Assessing Loss Using Data Analytics first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

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>

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/

<p>The post How Education Industry Can Utilize Data Analytics first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

Since the COVID-19 pandemic crisis, businesses across the globe decided to make a digital transition. In the quest for digital transformation, there are various tech innovations that can improve and evolve the use cases of data analytics for good.

Data analytics paired with AI and other technologies have made it easier to prepare, respond to, and predict data. In the event of a crisis, businesses can now be more proactive and maintain efficient and cost-effective operations. On the surface, it may seem like two or three tech innovations. But there is a long list of tech innovations that can fuel data analytics in the coming years.

Keeping that in mind, here are the essential tech innovations that have the long-term potential to transform and improve data analytics for years to come.

•  Cloud Services

As mentioned earlier, the public cloud has had an all-time high adoption. In short, the innovation of data analytics now ties together with the public cloud. You can expect more than 90% of data analytics to jump to the cloud. But the burden of responsibility now lies with data analytics leaders to ensure a flawless transition to the cloud and align data flawlessly.

Moving to the cloud means enterprises don’t have to deal with integration overhead and extraneous governance. As the public cloud evolves, data analytics leaders will be able to meet the performance requirements of the workload beyond traditional standards.

With the public cloud, data analytics leaders can better prioritize their workloads and make the most out of cloud capabilities. Moving to a dedicated cloud is a perfect approach to ensure cost optimization, accelerate operations, and drive innovation at the same time.

•  Visual Dashboards

Gone are the days of traditional dashboards that used to make data look far more complex to analyze. Today, analysts use personalized and automated visual dashboards with point-and-click features. In visual dashboards, the exploration and authorization are easier.

With more visual-based dashboards, the decline in traditional data dashboards is inevitable. Besides, visual dashboards allow analysts to paint a clear picture of the data, contextualize complex data, and spot interconnected patterns. Enterprises now want to leverage technologies like augmented analytics, streaming anomaly detection, and natural language processing.

With dashboards also create more dynamic insights and allow companies to integrate new technologies. In short, visual dashboards have made data analytics more context-driven and will pave the way for more dynamic and accurate insights.

•  More Practical and Responsive AI

Ai has become more responsive, faster, and smarter in the last few years. In fact, multiple reports suggest that over 70% of organizations will use AI technology to move their operations digitally by 2024. Furthermore, AI has had a significant impact on analytics infrastructure and streaming data.

AI subset like machine learning optimizes data analytics algorithms through natural language processing. These tech innovations provide accurate predictions and valuable insights that allow businesses to make countermeasures and ensure effective operations.

You can expect AI and ML to realign more data analytics processes and become more grounded. This will further make it easier for analysts to identify key patterns and establish connections across datasets. More AI and ML models will become highly transparent that will minimize poor business decision-making.

•  X Analytics

X refers to the unstructured and structured content analysis in the form of video analytics and audio analytics. This type of data is at the forefront in the digital age and businesses want to reap its benefits. X analytics has the potential to solve some of the most complex challenges of the world like wildfire protection, disease, disease prevention, and climate change.

After the pandemic crisis, experts profess that X analytics can contextualize hundreds and thousands of social media posts, research papers, and news sources. Similarly, healthcare institutes can use X analytics to predict capacity plans, disease spread, and figure out new treatments. X analytics, however, have to be paired with technology like AI to determine, plan and predict natural diseases for businesses and how to turn crises into opportunities.

•  Smart Intelligence

One of the tech innovations that large corporations use is to develop and improve their decision modeling is smart intelligence. In fact, analytics now practice decision-based intelligence that involves decision modeling. To make things simpler – decision or smart intelligence decision support and decision management.

Ultimately, these two aspects simplify complex applications and combine advanced and traditional disciplines. Smart intelligence is all about focusing on the “right” framework so that data analytics heads can create, design, model, monitor, execute, align, and execute processes and models in line with the business outcomes. It gives enterprises the chance to leverage decision modeling technology through mathematical tactics, logical sequencing, and automation.

• Augmented Data Management

Augmented data management is another tech innovation in the data analytics space. It uses a combination of AI and ML tactics to optimize operations. Also, augmented data management converts metadata for reporting and auditing purposes.

With augmented data management, you can examine an entire product and large samples of data. These samples can include performance data, queries, and schemas. Once you take into account workload data and current usage, augmented data management tunes overall operations.

It also optimizes security, performance, and configuration parameters. Analytics leaders can use augmented data management to simplify and compress the architecture of active metadata in the system. When it comes to conventional data management tasks, data analytics leaders can leverage augmented data management to increase the level of automation.

Final Thoughts

Ultimately, it dawned on companies the post-pandemic requires a reset and a swift way to handle data. In a traditional sense, businesses had no choice but to store extensive data in physical data warehouses. The major pitfall of this approach is that it incurs mountainous costs and offers redundant security at the same time.

Since the advent of hybrid cloud solutions, there is an innovation of 90% in the data analytics field. After all, cloud technology offers more flexibility, scalability, and efficiency to handle day-to-day business operations. With cloud-based data analytics alone, businesses can ensure agile operations.

Explore IDA further and find out how IDA’s data analytics platform is designed to stay on top of industry innovations.

REFERENCES:

1. https://www.hico-group.com/data-analytics-future-trends/
2. https://techbullion.com/what-are-technology-trends-going-to-shape-the-future-of-data-analytics/
3. https://www.forbes.com/sites/forbestechcouncil/2021/05/11/data-days-current-and-future-trends-in-ai-and-analytics/
4. https://www.gartner.com/smarterwithgartner/gartner-top-10-data-and-analytics-trends-for-2021
5. https://www.linkedin.com/pulse/top-5-big-data-analytics-trends-predictions-2022-arsr-technologies?trk=organization-update-content_share-article

<p>The post Top Tech Innovations that May Transform Data Analytics first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

Gov. Gretchen Whitmer will keynote the 2022 Michigan Counties Legislative Conference, addressing a plenary session of the event on Wednesday, March 23.

The 2022 event, co-hosted by the Michigan Association of Counties and the Michigan County Medical Care Facilities council, also will feature:

• A MAC Legislative Update, led by Deena Bosworth, director of governmental affairs
• Remarks from MAC President Phil Kuyers of Ottawa County and Executive Director Stephan Currie
• Breakout sessions on current challenges for county leaders, including workforce development, the Open Meetings Act, trends in mobility on Michigan roads, American Rescue Plan spending and how to deal with stress in these stressful times
• A Legislative Reception on the evening of Tuesday, March 22, during which MAC will present its County Advocate Awards for legislative service in 2021

The conference will be an in-person event, though MAC plans to Livestream select breakout sessions on Facebook.

Your conference registration fee provides access to all conference events, snacks at the Radisson Hotel on Monday afternoon, the Legislative Reception with appetizers and beverages, two breakfasts at the Lansing Center (Tues-Wed), lunch on Tuesday at the Lansing Center, and a boxed lunch on Wednesday you can take with you on your journey home.

“We appreciate the governor taking time to speak to our members this year as we continue to foster deeper state-county partnerships,” said Currie. “We are working closely with both venues to enhance COVID safety and will monitor state and local health directives that could affect the conference.”

What: 2022 Michigan Counties Legislative Conference

Where: Legislative Conference
(with MCMCFC)
Lansing Center/Radisson, Lansing

When: March 21-24

Monday, March 21, 2022

12:00 – 5:00 PM | REGISTRATION DESK OPEN | RADISSON

1:00 – 1:30 PM | MACPAC BOARD MEETING | RADISSON

1:00 – 2:30 PM | MCMCFC WORKSHOP: STRATEGIES FOR SUCCESS IN THE WAKE OF THE COVID-19 PANDEMIC | RADISSON

2:00 – 3:15 PM | MAC BREAKOUT: WHAT’S COMING ON ELECTRIC VEHICLES | RADISSON

2:00 – 3:15 PM | MAC BREAKOUT: TRIAL COURT FUNDING | RADISSON

2:00 – 3:15 PM | MAC BREAKOUT: HOW TO WIN MILLAGE ELECTIONS | RADISSON

2:45 – 4:15 PM | MCMCFC WORKSHOP: STATE LICENSURE AND FEDERAL CERTIFICATION REGULATORY UPDATE

3:00 – 5:00 PM | EXHIBITOR SET-UP | LANSING CENTER

4:30 – 6:00 PM | MCMCFC BOARD MEETING | RADISSON

4:30 – 4:45 PM | MAC BOARD LEADERSHIP | RADISSON

4:45 – 5:45 PM | MAC BOARD MEETING | RADISSON

6:00 – 6:20 PM | MCMCFC OFFICE TOUR | 110 W. MICHIGAN, SUITE 200

6:00 PM | DINNER ON OWN

Tuesday, March 22, 2022

7:00 AM – 4:00 PM | REGISTRATION DESK OPEN | LANSING CENTER

7:00 – 7:45 AM | EXHIBITOR SET-UP | LANSING CENTER

7:45 – 9:00 AM | BREAKFAST | LANSING CENTER

9:00 – 10:00 AM | PLENARY SESSION: LEGISLATIVE UPDATE, STATE OF MAC

10:00 – 10:45 AM | NETWORKING BREAK WITH EXHIBITORS

10:45 – 11:45 AM | MAC-MCMCFC JOINT BREAKOUT: OPEN MEETINGS ACT | LANSING CENTER

10:45 – 11:45 AM | MAC BREAKOUT: CYBERSECURITY THREATS AND TRENDS | LANSING CENTER

10:45 – 11:45 AM | MAC BREAKOUT : DEI IN COUNTY GOVERNMENT | LANSING CENTER

12:00 – 1:15 PM | DENISE WINFREY NACO FIRST VICE PRESIDENT; BOARD MEMBER, WILL COUNTY, ILL | LANSING CENTER

1:15 – 2:00 PM | NETWORKING BREAK WITH EXHIBITORS | LANSING CENTER

2:00 – 3:00 PM | MAC-MCMCFC JOINT BREAKOUT: STRESS MANAGEMENT | LANSING CENTER

2:00 – 3:00 PM | MAC BREAKOUT: OPIOID FUNDING | LANSING CENTER

2:00 – 3:00 PM | MAC BREAKOUT: PRIMER ON HEADLEE AMENDMENT | LANSING CENTER

2:00 – 3:30 PM | EXHIBITOR BREAKDOWN | LANSING CENTER

3:15 – 4:15 PM | MCMCFC WORKSHOP: STRESS SUPPRESSES SUCCESS: SHAPE YOUR STRESS BLUEPRINT | LANSING CENTER

5:00 – 6:30 PM | LEGISLATIVE RECEPTION | RADISSON

6:30 PM | DINNER ON OWN

8:00 – 11:00 PM | PRESIDENT’S HOSPITALITY SUITE | RADISSON

Wednesday, March 23, 2022

8:00 – 10:00 AM | REGISTRATION DESK OPEN | LANSING CENTER

8:00 – 9:00 AM | BREAKFAST | LANSING CENTER

9:00 – 9:30 AM | PLENARY SESSION: SPEAKER – ERIC FREDERICK, CONNECTED NATION | LANSING CENTER

8:30 – 10:00 AM | MCMCFC WORKSHOP: REGULATORY UPDATES AND OTHER SIZZLING TOPICS | LANSING CENTER

9:30 – 10:30 AM | MAC BREAKOUT: FUTURE OF COUNTY WORKFORCE | LANSING CENTER

9:30 – 10:30 AM | MAC BREAKOUT: HOW COUNTIES ARE INVESTING ARP FUNDS | LANSING CENTER

9:30 – 10:30 AM | MAC BREAKOUT: CULTIVATING AN AGRIBUSINESS ECONOMY | LANSING CENTER

10:00 – 10:30 AM | MCMCFC JOINT PROGRAM/QUALITY COMMITTEE MEETING | LANSING CENTER

10:30 – 11:00 AM | BOXED LUNCH PROVIDED | LANSING CENTER

11:00 – 11:30 AM | CONFERENCE KEYNOTE BY GOV. GRETCHEN WHITMER | LANSING CENTER

Source: https://micounties.org/conferences-2/#

<p>The post 2022 Michigan Counties Legislative Conference first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>

Data analytics continues to be a driving force for modern organizations to focus on valuable insights and ensure logical and efficient business decisions. According to a McKinsey report, various use cases and practices around data analytics have had a positive impact on businesses.

From small to large organizations, no organization wants to overlook data analytics. In fact, it is a crucial aspect to embrace digital transformation and accelerate business growth. Of course, data analytics revolves around numerous enables and parameters that often businesses.

Self-Service Analytics

Self-service data analytics allows non-technical users to connect to different data sources and build or analyze visual datasets. Many brands have impressive self-service data analytics strategies that involve various data governance elements.

After all, data governance makes sure the collected and shared information is accurate and represents top-notch quality control. At its core, self-service data analytics is all about data connectivity “after” reviewing key considerations through data analytics tools.

Integration Analytics

Data analytics has increasingly become more complex due to the integration of emerging capabilities like machine learning, and AI. Plus, the use of predictive modeling along with the cloud has changed the way organizations approach data analytics.

Many brands now use data analytics to inspect and figure out specific steps to make the “right” business decisions. More AI-powered BI tools in the marketplace mean data analytics will become more mainstream.

Augmented Analytics

Like integrated analytics, augmented data analytics involve machine learning. This practice makes it clear how specific content is used and developed over time. It is a tech feat that involves using analytical capabilities such as data management, data preparation, process mining, data science, business process management.

Organizations use embedded insights directly from augmented data analytics for their dedicated applications. Ultimately, augmented data analytics automates various processes and cuts out the need to hire data scientists. Augmented data analytics paired with a robust ML model makes it possible to build highly accessible data roles and ensure data scientists are productive.

Embedded Analytics

As the title suggests, embedded data analytics offers analytical functionality to business applications. The confines and parameters of embedded data analytics are predefined. In some cases, self-service business intelligence platforms come with embedded data analytic dashboards for user applications.

The fact of the matter is that it makes the entire process of data analysis straightforward and convenient. At the center, the focus of embedded analytics is to improve actionable insights. Embedding data analytics can also be part of the current data workflows to gain more capabilities. Nonetheless, this practice rewards users with informed and faster decision-making.

Advanced Analytics

Unlike traditional data analytics which utilizes historical data to ensure informed decision-making, advanced analytics has become a major practice that uses predictive models. In fact, organizations now use various predictive tools to create unique data simulations and predict future outcomes. But different scenarios tend to lead to different conclusions.

The goal of advanced analytics paired with predictive models is to use the information before key competitors. But the use case of advanced analytics requires a high level of accuracy and depends on granular data analysis. Mostly, advanced analytics involves making different kinds of assumptions through predictive analytics.

Cloud Analytics 

It shouldn’t come as a surprise that the cloud has managed to find its way into data analytics. When referring to data management and data analytics, cloud technologies continue to shape future trends. This data analytics practice points to the businesses that opt for cloud-based data analytics and business intelligence for their products.

This paves the way for cloud-based slash hybrid deployment through data analytics. Similar to self-service practice, cloud analytics heightens data connectivity, security, and governance altogether. Many cloud providers now provide complete cloud-based data analytics support. It allows vendors to organize, develop, and market a wide range of products through cloud analytics.

Final Thoughts

You may not be aware of it but the future of modern companies boils down to data. And data analytics involves accelerators, enablers, parameters, and insightful thoughts that favor data analytics projects.  AI-driven data analytics will become the center of attention and help companies make sense of complex processes. In 2022, using data analytics involves a wide range of complexities. With the support and use of data analytics solutions that accelerate the accessibility and simplify the complex amounts of information;  an organization can make the most out of data analytics.

REFERENCES:

1. https://ec.europa.eu/eurostat/cros/content/use-cases-and-best-practices-data-analytics_en
2. https://callminer.com/blog/data-analytics-tools-buying-guide-tips-best-practices-for-identifying-the-best-data-analytics-tools
3. https://hbr.org/2020/03/whats-the-best-approach-to-data-analytics
4. https://www.simplilearn.com/what-is-big-data-analytics-article
5. https://solutionsreview.com/business-intelligence/common-data-analytics-use-cases-you-need-to-know/
6. https://www.projectpro.io/article/5-big-data-use-cases-how-companies-use-big-data/155
7. https://www.qlik.com/us/data-analytics/big-data-analytics
8. https://towardsdatascience.com/best-practices-in-data-analytics-cfcb2baebcb3

<p>The post Common Use Cases and Practices of Data Analytics first appeared on Intuitive Data Analytics | Limitless Possibilities with IDA.</p>