Making Data a Corporate Asset

Companies that invest heavily in data management and analytics all too often don’t realize the full potential of the solution they are putting in place because they are focused on the newest technology, without understanding the underlying value in the data behind the technology. That includes large data warehouses, business intelligence, dashboarding, self-service analytics, predictive analytics, and most recently artificial intelligence and machine learning.

While there’s value in those capabilities, many deliver a sub-par return on investment. Root cause analysis often points to similar repeated shortfalls: a lack of maturity and capabilities in core data management fundamentals, particularly data governance, data quality management, and metadata management. It would be better to treat data as a competitive advantage and a key corporate asset. Advantages of doing so include top- and bottom-line growth in the form of expanded market share and increased profitability by using accurate and trusted data to gain strategic insights.

Additionally, data insights can provide operational efficiencies and cost reductions, while also streamlining operations in sales, marketing, and supply chain. Applying governance to data will also significantly reduce risks related to data breaches, help realize value for investments in data initiatives, and facilitate regulatory compliance.

The key to remember is that governance is the heartbeat that drives value from data.

Data Management: An Organizational Challenge

At its core, data management fundamentals enable organizations to create, use, and share information across the enterprise. That necessitates breaking down organizational and data silos. The ability to shift from data as a departmental asset to a corporate asset requires a change in mindset.

Let’s use the example of finance to illustrate enterprise data sharing. No one questions the CFO’s ability to define and manage the chart of accounts and general ledger processes and business rules. Individual groups don’t have the power to define revenue, cash, and depreciation differently—that’s the CFO’s organizational remit.

Data is different in most other groups, however, because all the associated data owners can adjust definitions, rules, and requirements based on their department’s needs, irrespective of the upstream and downstream implications.

“Data is pervasive across a company, with multiple owners, consumers, producers, and stakeholders, which makes it extremely difficult to build consensus without C-suite support,” explained Sila Data Management Specialist Mike Mueller. “This data pervasiveness also makes it very frustrating and time consuming to reach decisions on highly shared data elements.”

Other realities that make enterprise-wide data management a complex challenge include: a) cultural and organizational change management; b) rapidly changing technology platforms; c) exponentially expanding data volumes (Big Data); d) risks associated with data breaches; and e) disparate and disconnected IT systems.

Silos Make Data Disconnects Worse

Technology, process, and data silos all run counter to the true value proposition of data, which is to make it accessible and pervasive across the organization. To evolve into a corporate asset, data must be quickly and easily shared across business processes (with the appropriate security controls). This can be a challenging journey, but it is made much easier using a prioritized roadmap based on measurable business impact. This is the key to securing business buy in and unlocking the value proposition of your data assets.

“What we see repeatedly are reports built on incomplete, inaccurate, or stale data,” said Sila Senior Consultant Jeff Johnson. “Basically, you end up with pretty reports that the stakeholders do not trust or agree upon. The root cause of this is a lack of governance, a critical factor for driving consistency and improving adoption across the organization. Governance is a big gap.”

That’s a perennial problem in customer information, for example, whether it’s related to mailing lists, invoicing, or marketing campaigns. With customer data governance, companies can accurately connect customer information with software and product development, for example, so they can properly invoice and extract revenue with additional licensing and royalty management rigor. That’s often done today through costly, manual audits. The ability to automate the process could potentially lead to a significant amount of savings and revenue generation.

Use Cases for Data Governance

Fortunately, siloed organizations are ripe for a centralized data strategy, shared governance, and data management/quality methodologies to connect the dots across business teams. This presents tangible shared targets, especially for top- or bottom-line goals that lead to executive buy in. Because at the end of the day, data management implementation should be a business decision around market share, cost reduction, and revenue generation. It’s not a technology decision.

Use cases for data governance, data quality management, and metadata management are pervasive across all industries. Below are some specific examples across industry.

Enforcement of Service Level Agreements: Component-intensive industries rely on service level agreements to ensure parts are properly repaired and available to use. These agreements call for remediation when vendors are out of compliance. With data governance, manufacturers can enforce contracts by doing away with error-prone spreadsheets and by providing their vendors with accurate data they can use to stay compliant.

Supply Chain Management: Reducing maintenance costs is a recurring theme for organizations, especially those that operate long supply chain cycles like aviation. They need every tool at their disposal to ensure spare parts are properly stocked and positioned to avoid service interruptions. Data governance makes the huge amount of sensor data coming off modern-day aircraft manageable so that airlines can avoid AOG (aircraft on ground) events by anticipating maintenance needs before something breaks.

Control of Personally Identifiable Information: Numerous regulations around the world, including the new General Data Protection Regulation in the European Union, require companies to control who can access customer PII and how that data is shared. Having the right roles and groups associated with the PII is essential, and data management is the key process needed for compliance.

Rapid Product Release: The challenge of managing the rapid rate of change in taxonomy, definitions, and business rules (metadata) is a critical success factor to deliver rapid product release cycles. By streamlining the metadata management operations, organizations increase collaboration and knowledge sharing across their development teams and improve communications between the technical and business teams. Foundational metadata management operations enable data stewardship and improves product release cycle time across large, complex IT ecosystems.

These use cases are about sustainability and scalability. With data governance in hand, supply chain and maintenance operations, for example, can do away with quick-fix patches that only hold until the next crisis. Getting your data management fundamentals right leads to long-term benefits.

Tangible, Measurable Results

Implementing data governance, data quality management, and metadata management best practices are realized through tangible, measurable results at every milestone of a program. The key is to strike a balanced approach between near-term commitments and deliverables against the longer-term data management strategy, while addressing both top-down and bottom-up requirements.

“This is like an agile software development strategy,” explained Mueller. “Don’t try to boil the ocean. Start by fixing something that’s tangible with proven business value. Measure it, track it, and demonstrate it to your business teams. It’s easier to sustain ongoing and longer-term commitments with this approach.”

Begin the roadmap by baselining the as-is. Where are the business problems? Does it take an army of SQL developers to crank out monthly business reports? Are there regulatory compliance issues? Is there a customer data problem? Is there an invoicing problem?

“The business problem always comes first,” said Johnson. “And then you define and measure your progress based on objective data. This isn’t rocket science. But it shows you where money is being left on the table and guides you in fixing the problem. It’s not about writing a pretty report. It’s about creating something that you can act upon and measure progress through clear success criteria that makes a financial impact sooner rather than later.”

Takeaways

The steps needed to develop a data management strategy that breaks down silos, targets business needs, and shows results is summed up in three actions.

Understand that data fundamentals matter: Core data-management capabilities are the building blocks for any data-related initiative (and arguably any technology initiative, as well).

Data is a corporate asset so start treating it like one: Build the strategy and business case to evolve departmental data into sustainable and differentiated corporate assets.

Baseline and measure current capabilities: You can’t chart a course for business improvement if you don’t know where you are today.