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Data, data everywhere, but not enough to use!

Yes, it’s real! The big data tsunami is upon us, hitting everything from enterprises to entrepreneurs. According to International Data Corp (IDC), data creation will reach 163 zettabytes by the year 2025. That is a ten-fold increase over the 16 zettabytes of data created in 2016 as a result of automation. While enterprise data creation was only about 30 percent that year, the number is expected to double, with enterprise accounting for 60 percent of data creation by 2025. As these figures demonstrate, data remains the driving force behind nearly all human activity over the next decade. Yet, the majority of data between now and 2025 will not be produced by humans but by mobile devices, social media and sensors in everything from cars to water systems. And as the Internet of Things (IoT) takes hold and carries even more data into the cloud, data creation will only become more intense.

Data is critical to the success of every business. In the last 15 years, various companies have produced a huge amount of data, yet few know how to exploit that data to be more efficient or just to compete in the marketplace. Some Big Data facts published by Forbes reveal that by better integrating big data in healthcare, for instance, could save as much as $300 billion a year – the equivalent of cost reduction by $1000 a year for every man, woman, and child. Most of the gains will come from combining data generated by doctors in clinics with data from scientific research is supporting innovation in cancer research and informing more precise treatment plans. For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income. Moreover, retailers who leverage the full power of big data could increase their operating margins by as much as 60 percent.

Despite these huge potentials, only a tiny fraction of the data being produced (less than 0.5%) is ever analyzed and used, according to Forbes. Extracting business value from data still poses the toughest challenge to businesses trying to adapt in the new age of big data. With digital transformation, creating new types of large and real-time data across a broad range of industries, business intelligence (BI) and data management teams continued to be overwhelmed by poor, ill-defined data. But, according to industry experts, merely collecting more and more data, without a clear use or data governance plan, results in more cost and liability than benefit. It is the insights generated through analytics and combinations of different data sets that generate the real value. Enterprise adoption of artificial intelligence (AI) has set a huge amount of disruption in motion challenging companies to get their act together to understand what their datasets are and how they interact with the new business model.

To extract business value from Big Data, enterprises require skills, technology and an information foundation capable of handling high volumes and velocity of data. Organizations that leverage and mine their data predictively have a significant competitive advantage over their rivals, as they can gain important insights and react quickly to expand their business in a way that was not possible without predictive analytics. Today’s advanced analytics technologies and techniques enable organizations to extract insights from data with previously unachievable levels of sophistication, speed and accuracy. Here are a few things to consider when trying to extract business value from Big Data:

  • set clear business goals and priorities and ensure that the data you are processing matches those goals;
  • build your analytics capabilities based on your business priorities;
  • write a big data policy to establish the basic requirements for the general structure, format, identity, ownership, usage and access for all information within the enterprise
  • give your data context: the data to be mined must be clearly described by “tagging” it with meta data;
  • get some analytics applications – let your analysis team explore the processed data and visually inspect the data for patterns, trends, and clusters.

Getting business value from big data requires statistical, technical and business expertise. Even where the analytics tools exist, they must be tailored to achieve positive outcomes for both customers and the bottom line.

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