Insightful Data Analytics Demands High Quality Data
Much is written on “The Top 15 Data Analytics Trends to Watch” or “20 Things You Should Know about Data Analytics,” and so on. It is a big part of information technology and with so many points to consider, it could get confusing. Let us look at some of the trends that will affect any organization collecting and using data. Artificial Intelligence, oceans of data, cheap storage, and computing power have contributed to the boom in data analytics and all depend on high quality data to deliver the results companies expect.
BI Dashboards
The amount of data created, collected, imported, exported, and stored in the cloud is staggering. Dumping data into a spreadsheet and hoping to find some meaning is giving way to highly visual Business Intelligence (BI) dashboards. BI dashboard software allows users to see data in a simple presentation. With this technology, it is possible to analyze data and get answers almost immediately. A BI dashboard can show patterns, correlations, and exceptions that would take hours to evaluate with a spreadsheet or similar legacy tools.
Machine Learning
Machine learning models are created on-the-fly and analyze data quickly and with great accuracy. Machine learning is a subclass of Artificial Intelligence. The better the model, the better the analysis. Naturally, models must be built using the best quality data available. Models created with flawed data result in inaccurate conclusions which are then used to build new models.
With machine learning, companies can spot market opportunities and customer trends and tastes. They can react much faster than they could without an AI model.
Emphasis on Data Quality
The power to analyze data and present it in an understandable graphical interface only goes so far. What if you are analyzing outdated or incorrect data? The results are predictable: bad decisions. Data enters and exits organizations every day. It is critical to make repeatable business rules and protocols to build and support standards for data quality. Important data includes email addresses, physical postal and shipping addresses, mobile phone numbers, social media information, and more. Data quality tools from Firstlogic® and SAP® standardize and update all types of data. Once the data is sound, data management team members establish ongoing data quality procedures and a master data management program that addresses the entire enterprise.
Artificial Intelligence and Text Mining
With a powerful BI interface at their disposal, organizations will access more data from more sources. These sources include call centers, web inquires, chatbots, and other customer touchpoints. Artificial Intelligence (AI) sifts through these disparate sources and finds trends, customer preferences, response behavior, spending patterns, and more. Marketers can act on this information to anticipate customer wants and find new business based on current client behavior.
Text mining is a technology using machine learning to comb through documents – emails, blogs, social media posts, comment fields – anything that is text-based. A text-based document differs from a pure data-based document. If a web form asks you what state you live in and you click the NY choice, that is an example of a data field. It is easy to sort and quantify. If a form asks you to describe what you think about a product and how you use it, that is a text field. Text mining analyzes the text and provides information and insights not gathered from the data fields alone.
Predictive Analytics
Predictive analytics uses data, algorithms, and machine learning to determine future outcomes based on historical data. The goal is to identify how probable a past behavior will repeat and identify possible downsides. Predictive analysis is used in several functional areas:
Marketing – Predictive analytics optimize campaigns by gauging customer responses and promote cross-selling or add-on opportunities. Predictive models help businesses with customer retention and up-selling.
Operations – Predictive analytics create forecasts for staffing, inventory, industrial processes, and manufacturing. Predictive analytics can estimate market demand, so the company can optimize pricing and delivery for customer satisfaction, product availability, and profitability.
Risk Management – Credit scores gauge the buyer’s probability of default. It is a predictive analytic that has been used for years and is constantly tweaked as machine learning models make it better. The prediction is becoming closer to the actual results. A credit score created by a predictive model considers all relevant data affecting a buyer’s credit. That includes credit card behavior and other transaction events such as insurance claims, bank loans, and collections.
The Impact of COVID on Data Analytics
According to a survey conducted by TDWI (Transforming Data With Intelligence), Fern Halper, Ph.D., reported that 20% of data analytics projects were canceled during the pandemic, and about a third of planned new purchases for data analytics were put on hold. Not great news, but the big picture is that most organizations are moving ahead with data analytics plans, though new projects might be temporarily on the back burner.
Work on data analytics is likely to accelerate as companies emerge from the effects of COVID-19. According to Dr. Halper, “Data and analytics have become more high-profile across the company in helping to aid in making business decisions in a fast-changing environment.”
Summary
New developments occur rapidly in the area of data analytics. It pays to keep close watch on trends as they change and progress. Regardless of the ways companies use data to improve their businesses, the data on which their decisions and strategies depend must be clean, current, and reliable. Data quality steps companies take today will form a strong foundation for the advanced data systems of the future.
Firstlogic data quality tools include Address IQ®, DataRight IQ®, and MatchIQ®. Companies use this software to insure the data on which they depend is accurate and complete.
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