Author: Wes Flores
Publish Date: April 28th, 2016
Copyright 2016 by TDWI, a division of 1105 Media, Inc. Reprinted by permission of TDWI. Visit TDWI.org for more information
If you want to get the most value out of your data, you need to take inventory. A "data first" mindset is of utmost importance, because it will lead you to focus on your data as an asset. You inventory all other assets within your company; treat your information assets no differently.
As we know, it's not as simple as going to the single data warehouse to harvest data assets anymore, because the form and scope of your data have expanded. A data asset library will help you use your data as a major resource. Many are doing this informally on a project basis, but then the discovery process is lost. I suggest it's time to document your findings and expand the scope of the discovery to the enterprise.
As you prepare to inventory your data assets, focus on three broad areas to better organize your efforts: data source availability, data completeness, and data richness.
1. Data Source Availability
Does the data store have enough data sources? Step back and take a fresh inventory of the existing data sources accessible in the platform. Are you missing key domains of information that would help provide a complete picture of your enterprise? Ensuring you have insights into your data store can help identify your data source gaps. These gaps will help shape your data procurement priorities and strategy.
As you review existing data sources and data source opportunities, start with your operational systems that make your business tick (e.g., CRM, accounting, e-commerce, ERP, or MDM). There are many online and third-party data sources you should factor into your data source assessment as well. Here are some examples.
Social media analytics can help you find answers to questions such as: "What are my customers saying about my products and services?" "What are they saying about my company?" "Is it positive or negative?"
Clickstream analytics enables you to harvest information from your Internet presence. An analysis of click paths will reveal which products or services generate the most or least interest and can show the correlation between a marketing campaign and heightened interest in a product or service.
Third-party vendors are a valuable resource for purchasing information to integrate with your data to create richer meaning. Examples include business hierarchies, demographics, and information about competitors.
Public data is another great enrichment opportunity for your data. For example, foreclosure rates across geographic regions may provide valuable insights into product performance and investment strategies. Regardless of your line of business, you will find a wealth of public data available.
2. Data Completeness
Businesses are often hindered by incomplete information. This is a challenge that must be addressed when you design your data extraction process. Here are two key considerations.
Transaction completeness is an important design factor when procuring a new data source. Ensure you are capturing all possible transactions from your data source including any rare outliers. For example, a commonly missed data opportunity occurs if you only capture an end-of-day status from a system or exclude rejected records. The completeness of your data life cycle can provide additional insights into operational and customer-experience improvement opportunities.
Attribute completeness is another important design factor to ensure you fulfill your data needs for tomorrow, not just your immediate needs. An attribute's richness is determined by its relevance, timing, and integrity. Be sure to include all business-relevant fields in your data procurement processes to meet both current and future needs.
3. Data Richness
Evaluate your data for quality, timing, utilization, and integration. As you take inventory of your data, take time to update and expand your business glossary. This is a good way to keep your results centralized and managed within your data governance program, as well as help kick-start or develop this program. Use the following evaluation categories for data richness.
Timely data will ensure your data is relevant at the time your business users or operational processes need the data. Often, enterprise data warehouses will update daily, which limits the operational uses of your information. Identify your current data sources that need refreshing more often and adjust accordingly. Don't forget to factor timeliness into new sources.
Quality data is the bedrock of trusted data in your enterprise. As your information evolves, make it a key practice to uncover and tackle data quality issues with new and existing sources. Your data management practices should include a meaningful data quality framework to track and measure current concerns within your existing data. Lack of a framework can impact your data assets at a holistic level.
Utilized data means your data assets are being leveraged to provide their full business value. Ask these key business questions for this assessment: How much of your data is not being leveraged for operational or decision analytics? How much of your data is available in a mature BI tool with strong user adoption and metric governance? Is your data used to drive customer acquisition? Customer value? Customer retention? Customer experience? Is your data used to drive operational decisions?
Integrated data is required to ensure you gain the advantage of linking your data. While in silos, your data has moderate value, but integration of each domain increases its value exponentially. Well-integrated data is essential to deriving business value from your data. To maximize your data integration, consider putting effective conformity practices and master data management programs in place.
A Final Word
Assessing your data assets will uncover challenges and opportunities, but the benefits are far reaching. Doing a full inventory will put you on the right path toward strategic data management and will help you create a culture that promotes and rewards innovative and meaningful uses of your data.