How to use data analytics in business?

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Let’s take a look back to the 17th century when John Dryden wrote, “Anyone looking for pearls has to dive deep.” Although the author did not mean Advanced Data Analytics, this quote perfectly describes their nature. Based on our experience in data analytics and practical examples, this blog post discusses how deep you should dive into data to gain necessary and fact-based insights.

Data is the power behind every successful business. It is the fuel that fuels ideas and helps to grow a digital business continuously. Each day, more and more digital entrepreneurs understand the need to embrace a data-driven strategy as a means of making decisions rather than doing “guesswork”.

If you are not already using data for decision making, you are already late! No matter how big your business is. Today we have a huge amount of data available. But how do you process and measure this data? How do you leverage the benefits of data analytics in business to take it to the new level?

Methods of Data Analytics 

According to Gartner’s Analytics maturity model, there are 4 methods of data analytics, differentiated from the simplest to the most sophisticated. The more complex an analysis is, the more value – i.e. competitive advantages – it can bring.

  • Descriptive Analytics – This is also known as descriptive data analysis. It is about data from the past that helps answer the question: what happened?  For example, a healthcare project – see how many patients were hospitalized in the last month; a dealer – what is the average turnover per week; one manufacturer – how many items were returned in the last month, and so on. But let’s look at an example from our practice to illustrate this method: our team helped a manufacturer to make decisions on which category to focus on based on revenue, monthly revenue per product group, income per customer group, the total amount of metal parts produced per month.

Descriptive Analytics allows you to juggle raw data from multiple data sources to gain valuable insight into the past. But these results only show what is wrong and what is right without explaining why it is so. For this reason, data-driven companies use Descriptive Analytics in combination with other methods of data analysis.

  • Diagnostic Analytics – At this stage, historical data can be compared to others to answer the question: Why did something happen? With Diagnostic Analytics, it is possible to clarify causes and interactions as well as interactions, analyze consequences and identify patterns. Businesses are choosing this method of data analysis to gain deep insights into a specific problem. At the same time, a company should have detailed information; otherwise, the data collection needs to be done individually for each problem, which is very time-consuming.

For example: how a BI solution has enabled a healthcare customer to consolidate patient data from multiple health care providers on one platform, generate reports and dashboards with useful information, and then assess incident probabilities in other patients, thereby reducing risk.

  • Predictive Analytics – Predictive Analytics is there to look to the future, trying to figure out the following: What could or will happen in the future? Based on the results of descriptive and diagnostic analyses, this method of data analysis makes it possible to identify trends, detect deviations from standard values at an early stage and predict future trends as accurately as possible. Predictive Analytics uses sophisticated algorithms and advanced technologies to create future predictions. But although this method has many advantages, it is important to understand that forecasts are only estimates, the accuracy of which depends to a large extent on the quality of the data and the stability of the situation.

For example, thanks to predictive analytics and its proactive nature, a telecommunications company can identify subscribers who are most likely to reduce their costs and plan and execute targeted marketing activities to avoid this; a management team can weigh the risks with cash flow analyses and forecasts before investing in expanding its business. One of our practice examples describes how Advanced Data Analytics enabled a leading FMCG company to predict what to expect after changing brand positioning.

  • Prescriptive Analytics – Prescriptive Analytics is designed to literally prescribe: what action needs to be taken to eliminate or prevent a future problem and to fully exploit the potential of promising trends. An example of the Prescriptive Analytics from our project portfolio: A multinational company was able to identify opportunities for repeat purchases in its CRM system on the basis of customer analytics and sales history.

This state-of-the-art method of data analysis requires not only historical data but also up-to-date information from external data sources, allowing for the constant updating of predictions. It uses a variety of advanced tools and technologies such as machine learning, business rules, scenarios, and simulation models, neural networks, making implementation and management even more complex. For this reason, a company should compare the effort required with the expected value added before allowing this method of data analysis to come into play.

Practice the data analysis

Data analysis is not an activity that can be performed without specific skills. However, the Web offers data analysis tools that can deliver interesting results. Here, are some of the best tools for data analysis: 

  • Tableau – Tableau is a very effective tool for data analysis. The tool connects to multiple types of data sources, both locally and in the cloud, and provides an intuitive user interface that lets you combine, analyze, and present data in the best way possible. Tableau uses app integration technologies through API and JavaScript and integrates with the most popular enterprise management platforms.
  • RapidMiner – RapidMiner is an integrated data analysis platform that performs predictive analytics, data mining and visual and text analytics. It uses machine learning to improve the accuracy of the analysis. RapidMiner can be integrated with any type of data source, including Access, Excel, Microsoft SQL, Tera, Oracle, Ingres, and MySQL. The tool is very powerful and quickly generates reports on the results of the analysis performed by the platform.

Software for data analysis

Not just data analysis tools; there are programming languages that are particularly suitable for free, cross-platform statistical analysis. Here, is some of the well-known software for data analysis: 

  • Python – Python is an object-oriented scripting language that is quite easy to learn, with an open-source and cross-platform license. Python is especially useful for statistical analysis and offers many free libraries. It is a programming language with great potential for statistical analysis that is taught at universities.
  • R Language – R is one of the best-known data analysis software used in statistics and data modeling. R is a cross-platform and open source. The software also has programmed statistical analysis tools and allows you to create your own. R establishes itself in an academic environment and among data analysis professionals. R has nearly 12,000 packages and allows you to search for packages by category. R also provides tools for automatically installing all packages according to the needs of the user and also analyzes big records.

Conclusion

Today, data is an extremely powerful tool for growing your business and having more success. When used correctly, the data will show exactly which channels to allocate time and money to. Who should reach and how to get the most results? This is why it is known as “Data Analysis is the DNA of businesses.”

Depending on how deeply companies want to delve into data analytics and act on data, they can choose from a range of data analysis methods that best meet their business needs. Keep in mind that descriptive and diagnostic analytics are reactive, while predictive and prescriptive analytics are proactive. Meanwhile, the latest trends show that every year, advanced analytics is gaining in importance and offering more and more applications in different industries.

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