Are you winning the data intelligence game?

Many organizations are collecting and hoarding all sorts of data but completely failing to extract any real value from it. Successful businesses are employing data analysis for a tangible competitive edge.

data center
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When big data was hyped as the next technology set to transform the business world many organizations began to collect as much of it as they could lay their hands on. Data centers proliferated as companies sucked in data points from customer interactions, supply chain partners, mobile devices and many, many other sources.

It looked as though enterprises had jumped on board with the idea of big data, but what they were actually doing was hoarding information. Very few had any idea about how to unlock the insights contained within. Businesses that saw the value and pioneered analytics are beginning to see a major return on their investment.

In a global, cross-industry McKinsey survey of 530 C-level execs and senior managers, almost half said that data and analytics have significantly or fundamentally changed business practices in their sales and marketing functions, and more than a third said the same about R&D. Big data can have a major beneficial impact, but realizing those potential benefits requires a winning strategy.

Sitting on a gold mine

As the cost of data storage has declined and trends like the rise of the IoT have begun to generate even more useful data, the amount of information being collected and stored has soared. We can track things on a granular level today that would have been impossible in the past.

In retail, to give just one example, we can plot a customer’s course around a store or track their attention on a webpage. The gold mine that many organizations have been sitting on has grown enormously, but the value is buried in nuggets of insight that need to be extracted.

The cost of conducting analysis has dropped sharply with the advent of affordable analytics, artificial intelligence (AI) and machine learning. What used to be an intimidating capital and time investment fraught with risk is fast becoming an accessible strategy that’s now affordable, convenient and more flexible than ever. The companies that have successfully mined their data for insights have gained a demonstrable competitive advantage.

Organizations that have struggled with the logistics of data collation and analysis are measuring the cost and downsizing data centers. Because they’ve failed to benefit from big data so far, they are grossly undervaluing their data. This short-sighted thinking will be exposed in the mid to long term as their competitors make better decisions about where to move next.

Others have recognized the cost benefits of leveraging powerful analytics tools in the cloud, where the costs of storage and big data tools as a subscription merit the investment and provide a more palatable ROI for those who are serious about mining value from their business data.

Making swift informed decisions

The evidence that data-driven companies outperform the competition has been quietly amassing. A Bain report from 2013 found that while just 4 percent of companies were good at analytics back then, they were already twice as likely to be in the top quartile of financial performance in their industry, three time more likely to execute decisions as intended, and five times more likely to make decisions faster.

Making the right decisions about stock levels, marketing campaigns and new product developments is obviously vital for any business. Making those decisions rapidly, before your competitors, can drive timely innovation and the financial success that comes along with it. Being able to analyze your current operations and fine-tune them is one thing, but improvements in AI now offer the promise of reliable predictive analytics.

While investments in AI are soaring (just as investments in big data did a few years back), the same situation persists: many companies are not in a position to leverage the potential benefits. A prerequisite for coupling machine learning with automation to uncover important insights and act on them immediately is organized, accessible data.

Accessing your data

To store and sift through all of the data so that you can make it available when you need it, you must have a solid data management strategy in place. Chances are good that your data is spread across multiple data centers in different locales. Some of it is probably in the cloud, but it may be split between the private and public cloud, SaaS provider clouds and elsewhere. There’s also a good chance you fell prey to some of the common pitfalls when you migrated that data into the cloud.

It’s no longer necessary to set up a research lab and hire cutting edge talent to benefit from the enormous potential of artificial intelligence. The early adopters have done the legwork and there are many simplified, powerful cloud-based AI services you can readily adopt today, but you’ll need a way to get your distributed data in and out of those tools. To do that, a virtualized data layer will enable you to move data around globally into the cloud from factories, remote locations, SaaS providers and existing data centers. You will need to transform and route certain data to get it into the right format for the AI tools and to ensure you remain in regulatory compliance. If you want to harvest insights from your data, then start by ensuring that you can fully access it.

A winning strategy

When EY and Forbes surveyed more than 1,500 global executives last year they found that 70 percent of the most sophisticated organizations had already employed analytics to overhaul their business strategies. It has never been easier than it is today to unlock the value of your big data, which is not to say that it’s easy. The next logical step is to leverage AI and machine learning to harness the value that’s beyond human analysis due to its complexities and unknowns.

The right strategy begins with data organization and assessment. Figure out what’s valuable and make sure that it’s accessible. Find a flexible, affordable analytics platform that can help you mine it for insights. Consider machine learning, with expert assistance to begin with, so you can construct a framework for extracting even more value. Then work your way towards predictive insights capable of generating reliable forecasts that will dramatically reduce risk, improve the customer experience, and boost your bottom line.

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