The term big data tends to be overused in business today. While some refer to it as a technology and others a level of insight, it has come to embody many different data actions, from business intelligence, to analytics and data modelling.
We have become so obsessed with big data that we think we have to have this level of insight as a requirement to running a successful business.
And for the most part, that statement is true. Data has proliferated our society to the point that every decision is made with some influence of data. Certainly experience, gut instinct and advice play a critical role, but data has become one of our most constant advisors.
We rely on information at a business level for location expansion, product fulfilment, customer loyalty and marketing. But, that same feeling also translates over to our habits as consumers when we rely on data to help make decisions on where to eat dinner, where to buy a home, what businesses to shop with, etc. Data has really changed the way we operate as a society.
But as all of us jump onto this big data bandwagon, it is important to remember that big data is not always insightful. The speed at which information is gathered and the volumes we are dealing with today can make information more relevant, but it can also be riddled with errors. Big data has become too big for us to manage.
There is a high degree of inaccurate information in businesses today. Recent Experian Data Quality research reveals that almost all businesses have a problem with their data and on average, U.S. businesses believe about 30 percent of their data is inaccurate.
That is a shocking figure and shows the degree to which businesses trust their information. Without trust, business stakeholders certainly can’t perform big data analytical exercises and use them to make intelligent decisions about their business.
But why is that trust lacking? We frequently see that data can be inaccurate, incomplete or just unconsolidated for a full understanding of the customer. It also can go against the conventional gut wisdom, leaving some executives to disregard it entirely. We tend to like data when it agrees with what we are already thinking.
To develop trust around data, we need to realign our expectations. While a third of information may be inaccurate, what does that mean? Is the information you are actually trying to analyze inaccurate? Most of us do not touch the majority of our information assets for insight. So what does it matter if the information we are not accessing is accurate? We need to understand the true need for data in our business. We need to consider how to use data as a force for good.
What it really boils down to is being able to access, use and trust data. Information does not have to be perfect for us to achieve that and we don’t have to be able to utilize every data set within our system. To make big data work, businesses need to look at their own needs and decide what is good enough for them. What are the benchmarks within their business that they need to meet to trust and access information for analysis?
That means that organizations need to link data across channels and databases, put data governance practices in place and move quickly to ensure information can be used across not just IT, but also across various business stakeholders.
In a world dominated by data and technology, we are being forced to adapt. We need to make decisions based on new information rather than purely gut instinct, but we have to make sure the information we are reviewing provides the right insight. Too much data can be problematic. We can get bogged down in it and become unable to make decisions.
We have to sift out what actually makes sense to review and what we should discard. Big data doesn’t always have to be this massive effort. It needs to be small and manageable, fit for your business. No two big data efforts are the same.
Be sure that as you consider big data within your organization, you are ensuring the accuracy of information and that the data makes sense for each particular project.