Click here to learn more about author Sri Raghavan.
The enterprise of the future is built on data.
Today’s business leaders generally understand that data is critical to rapidly increasing revenue and profitability. Yet most businesses still treat data as a siloed commodity and manage it poorly, leaving many employees unable to access important data when they should be relying on it.
Future-focused enterprises need to shed this outdated mindset and begin taking the necessary steps to make data their most important corporate asset. The process starts with a re-envisioning of the data journey, followed by actually putting data to work through strategy, know-how, and foundational technology.
At the end of the process, data will be the central focus of a business – and a reusable data core is crucial to enabling this transformation. For those who may be unfamiliar with the concept, here’s a guide to why reusable data cores are so essential for enterprises of the future, especially as companies are transitioning to the cloud.
Rethinking the Approach to Data
Despite the general consensus that “being data-driven” is key to gaining a competitive edge, the hard truth is that most data enterprises collect remains unused. In some organizations, it’s quite likely that a majority of the data will never be seen, scanned, or even considered, either narrowly by specific stakeholders, or broadly by executive leadership.
The problem is straightforward: Far too often, organizations don’t understand how to process or optimize the massive volumes of data they’re collecting on a daily basis.
Under a traditional data-gathering approach, data becomes “owned” by specific business units that intend to use it for their own purposes. But isolated data delivers only the myopic understanding of information as viewed by a fraction of the business, as opposed to the company as a whole. This ultimately wastes resources and limits the data’s value, in turn preventing companies from solving ongoing problems and achieving high-impact business outcomes.
Further issues can occur when people and departments duplicate their data instead of sharing it, including data drift: Changes or discoveries made in one place but not another can result in inconsistencies, confusion, and a loss of agility in using the data. Simply put, siloed data cannot deliver full value when it lacks uniformity – the single source of truth businesses need to act quickly and comfortably.
As an example of how this works in practice, consider how business decision makers will react when relying on data that isn’t unified, or alternately, insights generated using only a small fraction of all related and available data. Their questions may be limited in breadth and sophistication, while their answers may wind up being incomplete:
- What products do we have in stock? (Inventory)
- What was sold? (Sales)
- Which business units generate the most revenue? (Revenue)
All of these questions are important but basic, and likely not much different than superficial questions competitors have also identified. Even assuming one obtains the right answers, they’re unlikely to lead to game-changing insights.
Designing Data With Reuse in Mind
Companies aspiring to become truly data-centric – and competitive based on their ability to generate predictive insights – need to take a critical leap: designing data with an eye towards reuse.
Instead of duplicating data for every use case, future-focused enterprises need to store data once in the data analytics ecosystem, then ensure the same data is accessible to everyone who needs it. The difference in approach may seem subtle, but it means moving analytics to the data, rather than the other way around.
This “store once, use often” strategy minimizes the costs and time associated with data acquisition, cleansing and transformation, data movement, and query processing. It creates a single source of data truth, enabling everyone within the organization to share and work off of the same information, rather than fractions or variations of it.
Integrating data and analytics also creates a strategic advantage, enabling companies to ask more questions, get more answers, and drive deeper insights. The more successful the data integration is, the more informed and sophisticated the analytics process will become.
Contrast the prior “basic data” questions and answers with the types of sophisticated queries business decision makers can pose when armed with all relevant information within an organization. Freed from the need to consider data silos, cross-functional questions can uncover a broader view of business conditions, such as:
- What are the trade-offs between holding inventory (Inventory), changing order (Order), frequency (Demand), and stock-out costs (Finance)?
- What is the profitability (Finance) of a customer (Customer) with at least one claim (Claims) over five years, by agency (Channel) and household policy ownership?
- Which person (Patient) should I prioritize (Risk) for an appointment (Schedule) for an MRI (Equipment, Location)?
By enabling exponential growth in question quantity and variables, integrated data reduces or removes limits on the answers that enterprises can unlock through data and analytics. This empowers businesses to gain holistic visibility into their operations and customer experiences, then identify new opportunities for improving revenue and market growth.
Preparing for the Future
While eliminating silos and integrating data are necessary to enable agile innovation, these first steps certainly aren’t the end of an enterprise’s data future-proofing initiatives. It’s impossible to predict every question that will need to be asked, or all the data needed to unlock the answers; similarly, enterprises cannot predict which data analytics dimensions they’ll need to scale, or when.
As a result, future-ready enterprises need to choose a data analytics platform that supports real-time, dynamic data exploration – a system that doesn’t limit the kinds of questions that can be asked, nor the moments when such queries can be made. Users must have the ability to ask any question, at any given time, and get solid answers derived from analysis of all data shared across the organization.
Only a multidimensional scalable cloud data analytics platform can achieve this. So equipped, companies can optimize their data across varied and dynamic core dimensions, including data volume, data latency, query data volume, query complexity, query concurrency, query response time, schema sophistication, and mixed workloads. Systems that limit their scalability to only a few of these dimensions at a time may diminish the capability of any of the others, potentially resulting in missed opportunities to leverage data and garner crucial insights.
Thinking Big Today Yields Dividends Tomorrow
Extracting maximum value from data requires a big picture, CIO-scale vision of how information should flow. Future-focused enterprises must expand data sharing from a single business unit to all possible stakeholders, walking a fine line between treating data as the most valuable and precious corporate asset, and making it widely available to everyone as the lifeblood of modern decision making.
Although it may seem like a heavy lift at first, the one-time shift from siloed data to a reusable data core will yield dividends far into the future of any business. Integrated data enhances both the quantity and quality of insights, leveraging large-scale context to drive action. When more sophisticated, value-driven questions can be asked at any time and by any person, an enterprise will be prepared to make rapid, informed, and differentiated decisions, then discover new avenues to compete and succeed.
Organizations of the future will go beyond using data to see what’s happening now; it will be the critical factor in deciding what should and will happen next. Business leaders who recognize this early will enjoy a huge competitive advantage, especially during times when insight and innovation are key to success. Over time, data will go beyond driving individual decisions and become the fundamental reason their businesses are leaders rather than followers in their markets.