Presented by BMC

The immense business value of data is increasingly quantifiable, a recent study by BMC and S&P Global found. Of course, it’s a complex undertaking, correlating upstream investment in data management with downstream business metrics. However, across the board, companies with mature data management practices are realizing measurable business outcomes and higher rates of success across multiple objectives, especially those that directly impact revenue and profitability.

“Data-driven insights are helping optimize operational models, powering up product innovation and dramatically improving customer-facing processes,” says Ram Chakravarti, CTO at BMC Software. “In fact, the study found that 77% of organizations with mature data management programs see significant increases in customer satisfaction.”

“Maturity” is the key word there, and it translates into an investment in DataOps methodology.

The DataOps difference

As organizations strive to optimize their data-driven strategy and technology, those without robust data management programs are running into a persistent gap, Chakravarti explains.

“Companies have a mountain of valuable data to mine – but they’re not actually realizing that value,” he says. Only 13% of respondents from organizations not using DataOps methodology strongly agree that they’re supporting business goals with data-driven insights, compared to 41% of organizations that are applying DataOps methodology across the organization to support their data-driven activities.

DataOps methodology adds agility and automation to data management, in the service of data-driven business outcomes. It requires some significant investments in technology and business process optimization, as well as talent and expertise. The sticking point for some companies appears to be justifying these investments out of the gate, when they can’t see immediate concrete organizational or operational outcomes or real-world ROI.

“Many of these initiatives are foundational in nature, which means they don’t really have a clear measure of success in and of themselves,” he says. “What does cleaning up and improving the quality of data in a particular data set translate to in terms of revenue uptake or cost reduction?”

Where enterprises are prioritizing their data investments

The study found that the majority of organizations have either “accelerated” or “sharply accelerated” investment in data management technology, while 31% have maintained their level of investment. And while businesses have a whole array of objectives for improving how they leverage data, 68% of respondents reported increased revenue and 55% said customer engagement and satisfaction are the leading priorities.

Data quality is the foundational requirement for data initiatives, and 38% reported that quality issues are their top challenge. Unsurprisingly, the most-reported factors driving adoption of data management technology are initiatives to improve data quality and integrity, say 57%. Building on that foundational gain, 53% report investing in developing business insights that result in revenue uptick , while 53% single out cloud migration initiatives.

But the pain points of data suppliers (33% of respondents) or those who pipeline, refine and stage data for consumption, and those in mixed supply and consumption roles (29%), reveal another investment story. Organizational investment can’t just focus on technology. Companies must also invest in the adjacent, intertwined issues with people and processes that cause the top issues these employees face. They include bottlenecks (30%), siloed data (39%), inadequate training and/or staffing (59%), and more.

The C-suite, disillusionment, and staying the course

Executive buy-in and support is one of the major differentiators between successful data-driven organizations and the organizations that are falling behind, Chakravarti says. Data management programs need champions at the executive level who are committed for the entirety of the initiative. Unfortunately, the onus lands on the team to deliver value early and often to prevent executives from pulling their early support.

“You want to identify the business value opportunities at the outset, and execute that stakeholder commitment at the highest level,” he says. “Start small and prove out the value. Keep your eye on the prize, and emphasize that it’s going to be a journey, but show you can deliver the wins systematically, and be laser focused on implementation and last-mile delivery throughout. Build processes slowly, adopt and adapt them as needed.”

That means not trying to boil the ocean, and instead starting with a use case-based approach. The key is a feasibility analysis, determining the relative value of potential use cases. High value, low feasibility is a tough nut to crack — go for the easier wins right up front, then systematically learn, adapt and operationalize. And if the results of your pilot aren’t worth the effort it would take to scale, it’s on to the next case, incorporating every best practice and insight you develop along the way.

Linking outcomes and refining data strategy

But ultimately, it’s not enough to put check marks on a to-do list of use cases; it’s crucial to determine your organization’s overarching strategy and end-game business goals. That typically translates into a set of outcomes: revenue uptick, productivity gains or cost reduction, and risk mitigation.

Once you’ve identified a set of use cases, and determined the data, analytics and processes required to support them, map how it all ties back to business outcomes and define a clear set of metrics that captures the impact on the business outcomes that your underlying data strategy provides. Optimally, every use case reveals a new area where tech investments should be prioritized.

“As you go, you’re finding the right horse for the right course, the right set of tools for each of these use cases,” says Chakravarti. “It cannot be an afterthought — it’s how you need to refine your organization or operating model to be truly data-driven.”

Setting strategy into motion

Implementation comes with its own set of strategies and challenges — as well as mistakes. And again, each is an opportunity to learn, refine and iterate. There are three horizons, Chakravarti says. In the near-term, you define a target state in terms of your process, architecture and implementation road map. In the mid-term, you’ll scale beyond those early use cases, across an entire business function. Learn from your stumbles, scale again, and operationalize — don’t declare victory prematurely, but continue to grow your expertise.

And then over the long-term, as you move into an assembly mode of execution, make it a business-as-usual practice, increasing your organization’s overall maturity and sophistication. When an initial investment results in actionable business insights and demonstrable ROI, it creates a virtuous cycle of long-term success, prompting ongoing investment in data-driven strategy.

“These are non-trivial investments,” Chakravarti says. “Data transformation, data management initiatives and data analytics initiatives cost significant investments of time, talent and revenue, often over multiple years. Many of these investments are foundational. But you can’t chase the shiny toy, and over-invest in one area while neglecting the bigger picture. You must be in it for the long haul, with a well-thought-out data strategy.”

Dig deeper: Go here for a closer look at the biggest takeaways from the BMC and S&P Global Market Intelligence survey.  

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