Data and analytics work with each other to deliver deep understanding, or insights, into your user base and company operations. Insights provide essential wisdom and reveal actions you can take to better your business. However, these insights cannot be obtained without analytics, and analytics is useless without data.
Data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize marketing campaigns and customer service efforts, respond more quickly to emerging market trends, and gain an important competitive edge over rivals—all with the ultimate goal of boosting business performance. Depending on the application, the data that’s analyzed can consist of either historical records or new information that has been processed for real-time analytics uses. In addition, it can come from a mix of internal systems and external data sources, and now, we can add increasingly accurate predictive capabilities into the mix.
Reality Check of Data Explosion
We know the volume of data is increasing at an incredible rate. We are often reminded in press and analyst reports that more data has been created in the last year than in all previous years combined. Such articles often are written in an excited tone based on the unstated assumption that more data will mean more value, more benefit to us all.
However, there is a huge downside to the “data explosion.” Many companies risk becoming data-rich but insight-poor.
- They accumulate vast stores of data that they have no idea what to do with, and no hope of learning anything useful from.
- To add to the problem, a lot of data has a lifespan. At some point in time, it becomes no longer relevant, inaccurate, or outdated. But often it is held onto anyway in the mistaken belief that someday it might come in useful.
- It’s important to remember also that collecting and storing data costs money: data requires storage and electricity to power it. And, if the information is sensitive, attention must be spent on security and data privacy, especially in the new world of enforced GDPR compliance.
- Of course, the problem becomes even bigger when we take into account the predicted growth in the data companies will produce. In short, if a company is already struggling to store and analyze its own data now, it will be drowning in data in the next few years. Pivot BI Analytics predicts that over the next three to five years, most companies will have no choice other than to commit to digital transformation on a massive scale, including fundamental cultural and operational transformations.
It becomes increasingly important for corporations, both from strategic and competitive points of view, to ensure that they are gathering as much relevant information from their data as possible. Old methods using sample data, old data, spreadsheets, and high-level reporting are simply not sufficient anymore. Manual methods of gathering data have not been sufficient for a long time, and yet many businesses continue to rely on these. After considering the points above regarding the appropriateness of data, it’s still absolutely true that there is value in a huge amount of the data collected by companies. But the correct data needs to be identified, collected, and analyzed to be able to provide increased insight.
Corporations need to move beyond manual methods
Automation is key! The automated collection of the right data, at the right time (perhaps in real time) transformed into easily consumable insights will make a difference. The combination of the right data, with the right analytics, made available to the right people in the right way can make an enormous difference.
Businesses can obtain significant value, perhaps differentiating value to ensure that they are aware of their obstacles and of their operational efficiencies, and in competitive markets, that they stay ahead of the game. However, given the amount of data being held within businesses today, this can be intimidating.
It can be difficult to know where to start, but you can feel assured in the fact that many companies have already started this journey. The analysis of massive data, from multiple systems, in real time, using predictive algorithms, is a reality for some. It’s available for all.