Delivering Big Data Expectations
Brent Preator, Sr. Director, Global Data Quality & Data Governance, JLL
Brent Preator, Sr. Director, Global Data Quality & Data Governance, JLL
Similarly, we hear stories of companies who jump in to big data, filling their data lakes with gobs of data, only to discover the data is disorganized, unusable, disconnected from the business strategy, and sadly isn’t used to inform key business decisions. This lack of organizational clarity and connection to business strategy is often a significant factor in big data failure. One of the best ways to create value for a company’s big data strategy is to connect it to the company’s critical data. Critical data is the business side data and represents less than 10% of the total data a company generates or consumes. Business leaders depend heavily on critical data to drive decisions, monitor key
processes, deliver products, and generate profit.
Big data must be connected to critical data to generate the insights business leaders depend on. Because we don’t impose a lot of structure on big data, critical data must have significant rigor and governance to make sense.
This is where critical data becomes important. The ability to generate high-quality critical data is essential to extract significant value from a company’s big data Identifying critical data starts by having stewardship discussions with key business leaders. In these forums, stewardship teams define and apply criteria to distinguish between critical data and other important data. Some potential criteria include:
• Critical to business operations
• Critical for benchmarking
• Governable / Standardized
• Critical for key business decisions
• Critical for regulatory compliance Why are these criteria so important?
Each criterion provides a unique and valuable perspective into the drivers of business success:
Critical to business operations –
Measuring business efficiency in areas like logistics, production, output, speed to market, turnover and sell through is essential. The most important task is to identify which values best measure and predict operational success.
Examples of critical data in this area may include inventory turnover, employee or manufacturing productivity, product backlog, cycle time, and click through rates. Critical for benchmarking – The critical data in this area provides industry-specific comparable data to measure performance of a single business versus its competitors. Leading indicators help companies determine whether they are gaining market share, improving performance, measuring successful impacts of strategies and assessing competitive trends over time. Examples of critical data in this area include comparable sales, gross margin rates, net cash flow, and debt coverage ratios.
Can be Governed / Standardized – It is essential for critical data to be governed and highly standardized. Having tight controls over critical data is an imperative. Consider financial, employee or product data that is not managed or governed. How much can you trust it?
Critical for Key Business Decisions
– This drives leadership decisions. Consider the data your business executives use to measure performance. Examples of critical data in this area include segment profitability, employee turnover, customer acquisition and expense savings.
Critical for Regulatory Compliance
– Critical data requires significant controls to ensure privacy and compliance to government laws and regulatory mandates. This data must adhere to new GDPR requirements, privacy laws, and security restrictions. Examples of critical data in this area include social security numbers, customer names, phone numbers and email addresses.
Ranking elements across these criteria isolate those elements that are truly critical. Notice that many of the data elements listed above would rank very high across nearly all the criteria. This distinguishes them as critical data.
Once a company’s critical data is identified and cleansed, align the company’s big data to the appropriate critical data to generate keen insights. Determine what transactional data, activity-based data or sensor data supports the critical data.
Here are a few examples of how companies have aligned big data to critical data to drive value:
• In manufacturing companies, establishing processes to capture real-time equipment performance data (sensor data) and tying it to asset information (critical data) can enable significant reductions in equipment lifecycle spend
• In retail companies, capturing real-time sales, and inventory data (transactional data) and tying it to item replenishment data (critical data) triggers auto-replenishment processes to maintain stock on high volume items
• In healthcare companies, capturing patient clinical visits and diagnosis data (activity based data) and tying it to patient spend (critical data) helps both healthcare companies and insurance companies to provide better care at lower costs
Achieving this level of competency is a game changer. Leading companies are doing this today. They have aligned critical data and big data. They possess the f lexibility to truly generate new, groundbreaking insights and are positioned to change the competitive landscape.
These companies are truly delivering on their big data expectations. They are creating superior value for their business leaders, customers and shareholders.
