The challenge of Big Data and manual analytical methodologies grows. With Big Data enterprise adoption rates growing year over year, companies face the daunting task of integrating and making sense of all this new information. Millions of tracked statistics, figures, and reports are coming in every week. These are a few of the many obstacles that stand in the way of harnessing the value of enterprise data.
For companies that look for real business value by analyzing this data, Verix has identified five tips to help you through this process.
Click through for five ways you can improve your data analytics, as identified by Verix.
Problem: Big Data comes from many disparate sources. Putting it all together is challenging. You need to normalize and analyze huge sets of data. However, pre-determining what’s important creates a silo effect – looking only at a narrow view.
Solution: “Correlations and patterns from disparate, linked data sources yield the greatest insights and transformative opportunities” – Gartner. Use a robust ETL system to take any possible data into account and logically analyze what is and what isn’t relevant. Avoid misconceptions due to lack of information. Findings are often quite surprising.
Problem: Big Data greatly increases the volume, velocity, and complexity of data streaming into the organization. Data comes from numerous disparate sources, which makes it harder to find correlations between seemingly unrelated phenomena. This is rather problematic when attempting to analyze this data.
Solution: By the nature of Big Data, collecting input from social networks, blogs, and public sources, along with your traditional customer, market, and operational data, results in significantly greater amounts of noise. Whatever is irrelevant to your goal analyses should be filtered out early on.
Heuristics based on domain expertise, along with time-series analysis, will throw out the noise yet leave in exceptions that might be important to detect trend breaks and quickly identify market changes and performance issues or opportunities.
Problem: Now that the user has gotten rid of non–relevant outliers and social media noise, how does one confirm whether a trend is problematic or just part of the norm?
Solution: The user must ask, is it unique in comparison with the behavior exhibited by all possible peer groups? In other words, is this happening everywhere else (e.g., other products, other stores, other geographies, competing products)? Segmenting an opportunity or a business issue is essential for turning this into actionable data.
Problem: Now that the user has verified a trend as a problem, they must dig further to establish why this trend occurred in the first place.
Solution: The user must ask why this happened. What are the possible drivers for this phenomena? Go deeper into the weeds, look at all related data, detect precisely what is unique here, and you will arrive at an informed analysis. Using domain expertise in this process is key.
Problem: One of the key reasons for many failed BI projects is the inability to constantly morph the solution as the business and the market do, creating a gap between the business and where the solution resides.
Solution: Be mindful that analytics are only as good as the data that comes in and if they’re not fresh, the results will be off. Stay on your toes. The user should strive to be able to quickly morph their analytics parameters as needed by the business. They need to be kept constantly up-to-date with the latest internal and external data.
An introduction of a new packaging size for your drug, a promotion by your competitor, a new oncology specialist in your territory, a new payer contract … any change in the business landscape influences the market. You ought to take it ALL into account to perform a meaningful analysis that will actually drive the business and affect the bottom line.