A guest blogpost by Emil Eifrem, CEO of Neo Technology.
Data is vital to running an efficient enterprise. We can all agree on that.
Of course, from there, thoughts and opinions differ widely, and it’s no surprise why.
Too much of the data conversation is focused on acquiring and storing information. But the real value of data is derived from collecting customer insights, informing strategic decisions and ultimately taking action in a way that keeps your organisation competitive.
Leaders who conduct this level of analysis distinguish themselves from the rest. Data followers merely collect; data leaders connect.
Yet, with so many ways to analyze data for actionable insights, the challenge is to find the best approach.
The most traditional form of analysis is the simplest: batch analysis where raw data is examined for patterns and trends. The results of batch analysis, however, depend heavily on the ingenuity of the user in asking the right questions and spotting the most useful developments.
A more sophisticated approach is relationship analysis. This approach derives insights not from the data points themselves but from a knowledge and understanding of the data’s entire structure and its relationships. Relationship analysis is less dependent on an individual user and also doesn’t analyse data in a silo.
Take a look at the biggest and best leading companies and you’ll see a strong investment not only in data analysis but also analysis of that data’s structure and inherent relationships.
For example, Google’s PageRank algorithm evaluates the density of links to a given webpage to determine the ranking of search results. Or consider Facebook and LinkedIn: each site evaluates an individual’s network to make highly relevant recommendations about other people, companies and jobs.
Together, these three organisations have developed real insight into their customers, markets and future challenges. In turn, they have become leaders in the Internet search, social media and recruitment sectors, respectively.
Every Data Point Matters
When it comes to effective data analysis, your enterprise must be gleaning insight from all of the data at its disposal, not just a portion of it.
With so much data to sift through, it’s no surprise that most organisations fall into a similar trap, focusing their data analysis efforts on a small subset of their data instead of looking at the larger whole.
For instance, it’s much easier for enterprises to only examine transactional data (the information customers supply when they purchase a product or service). However, this subset of data can only tell you so much.
The vast store of data a typical enterprise doesn’t use is known as “dark data.” Defined by Gartner as “information assets that organisations collect, process and store during regular business activities, but generally fail to use for other purposes,” mining your dark dataadds wider context to insights derived from transactional data.
Of course, data only tells part of the story with surface-level analysis. Enterprises need curious and inquiring minds to ask the right questions of their data. That’s why so many leading organisations recruit data scientists solely to make sense of their data and then feed these insights back to strategic decision makers.
Ultimately, the real value of data lies not only in bringing your enterprise closer to the customer but also to prospective customers. And building a better bottom line is something we can all agree on.