According to the Harvard Business Review (HBR), Big data projects that revolve around using data for business optimization and business development are top of mind for most executives. However, up to 85% of big data projects fail, often because executives cannot accurately assess project risks at the outset.
Having a data strategy means taking responsibility for having the right data, having the data right and for using it the right way to drive business and customer value, according to the Drum.com. The site’s proprietary research showed that nearly two-thirds of companies have not created an up-to-date data strategy within the last 12 months.
The HBR quoted Gartner study showing that the global analytics and business intelligence software market reached $21.6 billion in 2018. The firm has also predicted that, “through 2022, only 20% of analytic insights will deliver business outcomes.”
That means that organizations are investing billions of dollars in analytics with minimal return.
According to HBR, these companies are not answering key business questions (KBQs). Once you have compiled an exhaustive list of your KBQs, you should assess them along two axes: “ability to activate” and “potential to impact the business.”
For many organizations, activation, or the art of leveraging data to do something meaningfully different in the market, is the missing piece that bridges the divide between insight and business value.
How important is AI/ML in data?
According to Forbes Invest (FI), each day 2.5 quintillion bytes of data are created. There are 40 times more bytes of data stored on computers than there are stars in the universe!
Machine learning (ML) is swiftly becoming the gold standard for companies to meld their data hoard into profits, FI said.
In the past year, financial services companies have been rushing to integrate ML into their operations because it’s been discovered that by using literally thousands of data points on people applying for credit cards, car loans and mortgages they can raise revenue upwards of 35% without increasing bad loans.
ML “sees” connections among wildly different pieces of information that reveal people with the same credit score actually vary a lot in their likelihood to pay a loan. Even many people without credit scores are identified as good borrowers.
According to Computer Weekly, and based on the AI priorities 2020 study from PricewaterhouseCoopers (PwC), respondents rated data aggregation (45%), integration with analytics (45%) and using AI in internet of things (IoT) applications (43%) as their top three AI prerogatives for 2020, but only a third put taking a comprehensive lifecycle approach to data as one of their top three priorities.
Gartner Group projects $2.6 trillion in value will be created by ML and related technologies in coming years.
Quantum computing: A threat to data
Computers based on quantum physics are poised to open avenues in medicine, manufacturing, and finance, running massive numbers of calculations simultaneously at great speeds. Yet these emerging quantum computers’ staggering power to weigh possibilities could also render some of today’s data-protecting encryption schemes lame within a decade or two, touching off a race to keep business and government data safe.
The National Institute of Standards and Technology’s Post-Quantum Cryptography program aims to evaluate, stress-test, and ultimately publish by 2025 a new set of online encryption schemes that quantum computers can’t break.