How Digital Transformation Enables Big Data Analytics

There’s sometimes a misconception in the understanding of Big Data. Isn’t is just analytics? They’re related, but in this movement, data has become not only bigger but more complex meaning that anyone seeking to glean intelligence from Big Data must first store and translate it for business consumption. It’s a big job and it’s well beyond what’s humanly possible. Data is Distributed, Dynamic and Diverse (440x more data by 2020 from IDC Worldwide Big Data and Business Analytics Market Through 2022) and without technology, Big Data analytics is beyond reach for deep learnings in data analysis and science.

Just for the record, Gartner defines Big Data analytics as…

the autonomous or semi-autonomous examination of data or content using sophisticated techniques and tools, typically beyond those of traditional business intelligence (BI), to discover deeper insights, make predictions, or generate recommendations. Advanced analytic techniques include those such as data/text mining, machine learning, pattern matching, forecasting, visualization, semantic analysis, sentiment analysis, network and cluster analysis, multivariate statistics, graph analysis, simulation, complex event processing, neural networks.

How we got here

Analysts have been talking about Big Data analytics for many years, especially around Hadoop and the open-source distributed storage technology. But with computing moving to the cloud and the enterprise rapidly adopting artificial intelligence and advanced machine learning (or deep learning), we’ve now set the stage to analyze distributed, dynamic and diverse data from any source and and get answers from it in minutes. But becoming a data-driven enterprise requires a change in both culture and technology to realize the new benefits that Big Data analytics brings—speed, agility and security.

What is digital transformation?

Overused and quite nebulous these days, the term has become a bit of a euphemism for data infrastructure modernization. But that’s not all bad. When Big Data becomes a force that can’t be ignored, companies realize the true meaning of these mainstream terms. It’s a trigger for fundamental change affecting how organizations organize around their use of technology, data and people/processes. So real that 40 percent of all technology spending will go toward digital transformations, with enterprises spending in excess of $2 trillion through 2019, according to IDC.  

Why digital transformation is so important

Big Data analytics allows companies to quickly access and analyze their data to make better business decisions—faster. Smarter business decisions yield more efficient operations, higher profits and loyal customers. Data-driven enterprises are looking for the following value drivers:

1. Control Complexity & Cost of Analytics. Big Data technologies such as cloud data warehouses and and adaptive analytics fabrics bring significant cost advantages and predictability when it comes to storing and querying large amounts of data.
2. Accelerate Time to Better Insights from Data. With the speed of cloud-based analytics processing combined with the ability to source and query new sources of data with an adaptive analytics fabric, businesses are able to analyze information immediately and make decisions on what they’ve learned.
3. Mitigate the Multitude of Risks Associated with Analytics. With the reduction of complexity that a universal semantic layer provides comes the reduction of risk of data errors, and an increase in governance through the use of policies–all without business disruption.
4. Gain One Consistent & Compliant View of Business Metrics. With the ability to predict customer behaviors through unified analytics comes the ability to provide customers with superior customer service on demand. Moreover, these insights can discover new product innovations to meet specific customers’ needs.

What digital transformation means for the enterprise today

Digital transformation is still the major driver in the market that’s forcing the hands of legacy platforms and the mass migration to the cloud. The promise of realizing what data can actually do to advance business decisions and actions has still eluded the enterprise. It’s part culture (focusing your company on mining and benefiting from its second-most important resource after its people—its data) and part technology.

Big Data’s power does not erase the need for vision or human insight.
—“The True Measures of Success” by Michael J. Mauboussin

The technology available to handle the volume, velocity, and variety of Big Data takes incoming streams of data, unifies the semantics and virtualizes it for ease of consumption/analysis in real time—ultimately distributing it onto cheap and fast storage without the need for additional data engineering because the data is singularly integrated and secure. Technology is always a necessary component of a Big Data strategy.

Digital-born data-driven companies such as Salesforce and Google have been investing in both the organizational and technological components transformation, and many of the Global 2000 are just starting on their digital transformation journey—dipping their toes into data virtualization and cloud computing. What they’re running up against are obstacles and hurdles in both human resources and technology. With more data comes more complexity which requires more manual intervention from IT resources and data scientists (both are in short supply), and as a result enterprises are coming up short in their cloud and advanced machine learning or deep learning investments. But the investments are worth it.

Companies in the top third of their industry in the use of data-driven decision making were, on average, 5% more productive and 6% more profitable than their competitors.
—HBR Big Data: The Management Revolution

The inconvenient truth

Unless you are a massive digital-first company, your Big Data analytics strategy is important only on the dimensions of data, adoption & reach. Your competitive advantage is first and foremost your data, secondly how to leverage the speed, agility and security that digital transformation can bring, and thirdly what percentage of your workforce has the ability to use AI/ML to make better decisions and drive better outcomes using Big Data.