by Susan Hurst
How much is your organization’s data asset worth? Well, how much business would you lose if your data were stolen, sabotaged, or became obsolete? That’s how much your data asset is worth. The real business world consumes data as humans consume food for fuel and good health, so you must understand how to protect your data asset so that it can fuel the delivery of your products and services on time.
Don’t Get Left Behind
Misinformation and silent information prevent your data asset from providing the right answers before you make critical business decisions. Most misinformation is caused unintentionally by well-meaning workers who want to do their jobs properly, so they will come up with their own solutions in the absence of information that your information systems do not provide. Although the motivation of these workers is admirable, their solutions are isolated from your overall business plan. They are outside your organization’s data asset, which is your source of information for getting the answers you need. See The Left Side of Monday to discover more warning signs of trouble.
Employ Data Science
The solution is a single source of truth which must be established and protected by aligning your IT (Information Technology) practices with the interactions of all organizations within your supply chain. All components must work together to produce your quality products and services to your customers. Protecting your single source of truth opens the door to superior analytics provided by data science.
Searching for answers to your tough decision options is the specialty of data science. Data science is the practice of diagnosing and responding to time-sensitive trends and conditions in a body of data. Specific responsibilities of data science in a business environment are to:
- Create logic behind the data that leads to business decisions.
- Model and mine in big data environments.
- Extract data, formulate models and apply quantitative analysis in a proactive manner.
- Understand the anatomy of the organization’s data asset.
You need data science to shine the lights on the road ahead as you plan for the uncertain future.
Single Source of Truth
Here is an example of a problem solved by establishing a single source of truth from multiple sources. A data delivery company (DDC) had contracts with ~180 customers, all of whom were utility companies, to collect meter and device power consumption reads via RF (Radio Frequency). DDC delivered the read data daily to the utility companies electronically. Each utility customer would use that read data to charge their own customers for their power usage. Although DDC had lots of data to analyze, all of it was siloed. Someone had to query each utility company’s database separately, then pool the output together to do any performance diagnostics or analysis. The separate databases themselves were not the problem…each contained source data that did not exist elsewhere. Assembling all the source data in yet another place was the problem.
The solution was to create DREs (Domain Rules Engine software), to link one or more of the target utility company databases to the governing database so all the data could be gathered at once. Big data tools were employed to do the analysis and reporting. No copies of data were needed because the governing database used the source data directly from each source data repository. Now, DDC managers have dashboards and reports for the entire population of utility companies that they serve. All the output is available at any time online via the company’s intranet. Managers have multiple views of the customer information available to them, any of which may be exported to Excel, PDF or another file format. They can drill down on any detail that they want to examine more fully.
Make It Right!
Markers of best data practices include:
- Clear business rules for powering the “switches” that direct data to their expected destinations.
- Single source of truth for each data element in an organization’s CLEAN (Consistent, Live, Enduring, Accessible, Navigable) data.
- DREs that manipulate your organization’s data according to business rules and processes, untouched by human intervention.
- Data Factory that employs DREs to produce repeatable processes for the production of reliable information.
- Supply chain management DREs for transporting data and information.
- Information Store that presents ready-to-use information for your organization.
- Data science practitioners to provide simulations, statistical analysis, and an adaptive clock to let you know if you are still in the game or if the game is over.
Rescuing your data asset requires unifying your business processes with your IT processes, as described in The Left Side of Monday. Use your leadership expertise to promote your Data Factory, Information Store, and Supply Chain Logistics practices to the level of professionalism used for producing your organization’s products and services.
Susan Hurst is a contributing writer from St Louis, MO.