How to Get Started with Data Analytics and Why You Need it

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Data Analytics

Data Analytics for Better Business Intelligence

Data is king in the modern business world. Thanks to technology, collecting data from just about any aspect of a business is possible — including tracking customers’ activity, desires and frustrations while using a product or service. This means business leaders can make tangible decisions and shift goals based on near statistical certainty, but it’s not as simple as it may sound. Many companies are analyzing millions of data points at any given time and making sense of all of it can be challenging. This is where the art of data analytics comes in.

What Should Your Company Know About Data Analytics and Business Intelligence?

Data analytics enables businesses to interpret and analyze a significant amount of raw data to help them make strategic decisions that drive revenue, reduce costs and create better products and services. Using a blend of technology and the expertise of data analysts, data is often presented visually in the form of charts, worksheets, graphs, etc. that make it easy for business leaders to digest information and make decisions that will drive value.
Let’s examine the different types of data analytics, the benefits of the approach, how businesses can get started with data analytics, and how to avoid common mistakes.

Types of Data Analytics

As explains, data analytics is a broad term including the following subtypes:

  • descriptive analytics
  • diagnostic analytics, predictive analytics
  • prescriptive analytics

All of these types of data analytics rely on data warehousing and machine learning. If descriptive analytics describes what has happened over a given period of time showing, for example, how website views have trended over time, diagnostic analytics shows mainly why something has happened. This involves more diverse data inputs and some hypothesizing.
Predictive analytics analyzes and presents what is likely going to happen in the near future and prescriptive analytics suggests certain steps based on the conditions of the situation.

Benefits of Data Analytics

  • Clear data overview

    Data analytics implies data exploration, helping organizations to understand what is inside the entire database. It helps businesses to see all the data they have, making it transparent and visible to the entire organization. For example, as organizations start to gather data, the information is often scattered without structure, analysis, etc., which makes it difficult to process the data and make strategic conclusions. So, to know what data is available and in what structure it is organized simplifies the overall business processes and makes it possible to see the whole picture in a clear and transparent way.

  • Customized visualization

    By using Extract Transform Load (ETL) procedures data engineers help to prepare data in the most valuable way, being able to extract necessary information exactly when it is needed. For example, a company may have millions of lines of data in its database, but business leaders need a summary report for just the previous month. Data analytics will help to transform all the information available in the requested view. Business leaders are presented with images, reports, diagrams and qualitative insights to help them shift strategy appropriately.

  • Better business conclusions

    Every business has its own KPIs and data analytics helps to make better business decisions because statistical information is processed and analyzed in a way that is meaningful. For example, there may be data on the number of unique users and the percentage of users who paid for a product. With data analytics, it is possible to pinpoint how much revenue to expect based on user data, which will ultimately help to make better financial decisions. Data analytics is also helpful for carrying out marketing campaigns and analyzing seasonal prices and trends.

For more details, check out our “ROI of AI” article.

How to Get Started with Data Analytics

The root of data analytics is, well, data! So the first step is data collection.

For those just getting started with data analytics, it is advisable to first compile the data that is most critical for key business outcomes. Also, collecting the right amount of data is an important first step for growth. Collecting the right amount of data helps generate the most streamlined reporting and data insights. For example, a retail organization has a set amount of goods, lists of purchase history, point of sale history, customer engagement history, etc. In this case, it would be best to pick just a few data points that are most critical to the bottom line.

When it has been decided what data to collect, the tools needed depend on the amount of data and the size of the business:

  • At the onset, many companies may start collecting and reporting on data using Excel, Google Sheets, or other free data recording tools.
  • When the quantity of the data begins to scale, free data analytics tools simply can’t process all the data that is available. In this case, a database is needed, like Microsoft Access, to store data, and Excel or Tableau/SAS to analyze it.
  • When it comes to collecting gigabytes of heterogeneous data, Big Data technologies like Spark or Hadoop would be needed.

It can be challenging to know which approach to choose, so do not hesitate to consult data engineering specialists to choose the right tool and avoid extra expenses.

The main takeaway here is that there are several ways for businesses to work with data. The information may be used as it is or data warehouses may be built. Usually, large enterprises that have many transactions and other data are aiming to save their information in data warehouses. But, the most common way to store data is a hybrid approach. The data is compiled in various sources: cloud, hard drives, social media, and later it is possible to see what is needed or not to generate necessary reports or extract needed information.

Data Analytics Implementation: Next Steps

Despite step 0 being common for everyone, the next steps vary a lot depending on the problem you are going to solve. For instance, for Descriptive Analytics you might need to apply data aggregations and build dashboards with charts, for Predictive Analytics you might need to apply Machine Learning to build a usable predictive model.

The best suggestion here is to work closely with Data Analysts and Data Scientists to help you make decisions.

How to Avoid Common Mistakes with Data Analytics

  • Keep it simple

    Work with the data that is available. If there are one million lines of data, don’t build a data warehouse just yet, use any visualization tool and a database, or just save everything in CSV files. Once there is a larger amount of data, then start thinking of building a data warehouse.

  • Set clear goals

    Determine the challenge that needs to be solved in advance. Data analytics should not be utilized by an organization for no reason, its purpose should have clear goals that help to solve specific challenges. For example, an organization may want to predict market demand for a product so they know how much of a product to make and predict the size of the potential customer base. In this case, gather the data that will help to analyze the market and it will help predict demand.

  • Consider using existing solutions

    There is a range of solutions for working with data like Tableau and SAS software. These solutions help to retrieve data from the database and build infographics and charts, providing analytics of the raw data you possess. ERP vendors (e.g. SAP) can provide Data Analytics capabilities too. So, consider using the existing Data analytics solutions before implementing new complicated ones. This will save your time and effort.

  • AI can help, but tread lightly

    There are ways to solve issues with data with machine learning technology. AI algorithms may help with data analysis to save time and effort on repeatable tasks, but people often think that implementing an AI solution can solve all the challenges they have. But, AI and machine learning can’t solve every problem when it comes to data and analysis. First, consider reaching out to data experts: Data Scientists, Data Analysts, Data Engineers — who will help get organizations on the right path and advise on which approach is best to use.

Overall, data analytics solutions leads to better business intelligence. Collecting and analyzing data in the right way can help business leaders pivot strategy and goals to increase business value. While data analytics can be a challenging and often time-consuming process, it is worth it to invest in the right tools and expertise to get it done right.

Authors: Alexandra Motulskaya, Siamion Karasik

Exadel has extensive experience working with Data Analytics, designing secure solutions to meet business intelligence needs. We can help enhance business processes based on digital insights, help with decision-support applications, and help integrate them with common language apps.

Check out our website if you want to learn more about Exadel Big Data & Analytics and AI & Machine Learning expertise along with related case studies. Or get in touch with the Exadel team to speak with our experts.