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Operations vs. Insights: How to Make Sense of the Right Data

  • Robin Purohit
  • November 21 2016, 6:30am EST

Having the right information at the right time is an enormous competitive advantage in today’s marketplace. The ability to analyze huge, varied and rapidly evolving datasets can propel organizations ahead of their competition when used to develop a greater customer-centric approach, hone internal processes or uncover new business opportunities.

IDC predicts that by 2020, “analytically oriented organizations will achieve an extra $430 billion in productivity gains over less analytically oriented organizations.”

To gain value, organizations need to consume, manage, compute and understand their data in order to make the data-driven decisions that will impact their top and bottom lines. With data growing at an exponential rate, the concept overwhelms many companies.

More digital data was created in the last two years than in the entire previous history of the human race, according to the Scandinavian research group SINTEF. Yet despite an ever-increasing data lake about their customers, competition and internal operations, companies are failing to unlock insights. IDC surmises that less than 0.5 percent of all data is currently ever analyzed and used.

Operational roadblocks hold many organizations back from diving into the data lake and surfacing with insights. As many as 60 percent of big data projects never go beyond piloting and experimentation, according to Gartner.

To be successful, companies need automation processes together with the right strategies that allow data teams to focus on big data insights, not operations.

Operationalizing big data projects

Establishing and managing big data projects in a production environment presents a variety of challenges.

Accessing the right data for analytics relies heavily on integration with the rest of the IT ecosystem. It’s critical to take a unified, holistic enterprise approach to automating real-time and batch processes that span multiple platforms, applications and workflows. Automating processes such as data ingestion from multiple sources within this holistic approach is also faster than manual methods, reduces risk involved with data privacy, and ensures that big data projects are scalable.

Another challenge is streamlining operations management across big data, as well as the broader, existing IT environment. Companies should visualize capacity needs and application performance bottlenecks in order to accurately allocate IT resources for new and ongoing services.

Beyond these technological challenges, becoming a data-driven enterprise involves cultural changes too. The executive team needs to buy into the data-driven approach. Team leaders need to know the company’s success metrics, what teams are being evaluated on, and how to measure it.

Companies should aim to get some quick wins from data-driven results that will prove the value of a data-driven approach. A best practice should be set so no major business decision can pass unless there’s data to back up the recommended actions.

Big data, big insights

With the right strategies and automation processes in place, companies can dive into insights. Big data analytics can be used well beyond simply reporting what’s happened. It can accurately predict future outcomes and behaviour to strategically recommend actions that drive better and faster business outcomes.

Enhancing the customer experience

Improved customer experience is often the most desired business outcome of big data analytics projects. The right data can be the golden nugget that helps companies understand their customers better.

Mining data from different sources, including online and social media mentions of the organization/product and sales history across multiple touch points, paint the most accurate view of customer profiles, their opinion of a brand, and their needs, wants and expectations.

Data decisions can drive a customer-centric business in multiple ways, from re-targeting customers to reducing customer churn to engaging audiences across channels for a consistent, seamless customer experience.

Speeding and feeding innovation

In the same vein of driving business growth, big data analytics can help enterprises identify new areas for innovation – markets, products and business models – faster than the competition. Navistar, a leading manufacturer of commercial trucks, buses, defense vehicles, and engines, captures data from over a dozen telematics providers to create 20 million data records per day.

With automation processes to collect and transform big data and the right strategies, the company has seen five times faster creation of actionable data. This data is used for new, value-added services that empower truck drivers such as remote diagnostics enhancement, and vehicle quality improvement.

Data and insights will continue to grow in importance. By 2020 about 1.7 MB of new information will be created every second for each human, according to IDC. The more volume, velocity, and variety of data that is introduced into the organization, the greater the need for a manageable and scalable approach to operating the big data environment. Automation and devising the right big data strategies is key.

Mastering big data for analytics is fundamental to every organization's ability to survive and thrive amidst competitive pressures. Companies must take action so operational roadblocks do not hinder insights.

(About the author: Robin Purohit is group president of the enterprise solutions organization at BMC)

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