Today, analytics-driven best practices dictate that, content collecting portals and other data-centric projects undergo the process of data cleaning and enrichment because analytic-oriented failures (i.e. descriptive, predictive, and prescriptive analytics models or reports) are often linked to the quality of data. To halt the perpetuation of bad data across the enterprise, initial “clean and enrich” initiatives have been effective including gathering missing or incomplete data, identifying duplicate suppliers, standardizing names and categories, and establishing supplier dependency and corporate global presence linkages.

Fortunately,  small, medium or large firms can engage external firms that advertise data enrichment services that provide a centralized view of suppliers, improved supplier data clarity and managed spend and discount intelligence per supplier. Cautiously, organizations should be prepared to choose from a typical ABCs in the field.

A.      Risk Data

Commonly, this option is used as the lynchpin or depending on your perspective – the hook. Basically, the firm provides supplier risk (often based on some proprietary ratios, algorithms or assumption) related to their interpretation of data. The claims often cite that risk data fields are designed specifically to assist purchasers in identifying supplier risk level.  Furthermore, many claim their data has validity or worst – generality because they are based on lien and judgment information.

B.      Diversity Data

This option promises to provide the largest and most accurate database of diverse suppliers with detailed diversity certification information.  Firms with this offering will often state they are leveraging all of the major supplier diversity certification agencies as data sources in order to maximize data accuracy. For instance, the will indicate you can easily identify HUB Zone members, DBE, MBE, WBE, LGBT, SDB, 8(a),  veterans of armed forces, service-disabled veteran, and others.

C.      Enterprise Data

Interestedly, this option will include detailed supplier data as part of a large enterprise-wide content packet. The value-added claims included detailed supplier hierarchy, resource allocation percentage and detail facility (i.e. square footage, census traits, years of occupancy, economic statistics and location) information. This offering will include classification codes for each supplier including local or municipal commodities, standard industry codes (SIC) and North American Industry Classification System (NAICS) codes. In practice, this allows broadens firms' ability to perform multidimensional analysis that that include market, sector or industry dimensions.

Depending on the business model of the service provider, the  data offering maybe packaged with software application or engagement features and benefits including;

• Supplier data that has been aggregated from over a thousand data sources covering a percentage of cities, counties or countries.
• Enterprise linkage insight that includes parent and child relationships, headquarters and remote locations and joint and consortium arrangements.
• Complete organization diversity information (i.e. race, ethnicity, birth origin, native language, immigration status, etc.).
• Immigration Visa status,  Employment and Education Visa status including  expiration dates).

Clearly, the aforementioned type data has enormous potential without including the dimensions of Big Data (the subject of future publications from the Analyticship series). Thus, it becomes apparent why interest in supplier diversity has increased with the advancement of information communication technologies (ICT) relating to Business Intelligence and Analytics. First, a key contributor of the interest is risk and compliance issues and governance. Basically, supplier management requires decisions that must be focused on managing corporate governance, performance, risk, and compliance with external partners that are proactive to avoid economic blind spots. In addition, analytic-driven management allows monitoring suppliers to gain enterprise intelligence that puts the right information in the right hands at the right time.  

Second, the realization of cost reductions by sourcing effectiveness through linkage of potential spend leverage, reduced cycle times, duplicate process removal and process automation. In addition, with the inclusion of end-user configurability there are reductions in the dependence on time-consuming, costly ICT programs and personnel (these types of advancement has definitely impacted my engagement load).

Alternately, small, medium or large firms can engage external literature sources to better understand the nuances of data enrichment, familiarized key internal staff with the data enrichment terminologies or to develop internal processes to implement using internal resources exclusively. One of the key challenges for this alternative is avoiding being misled by the names, labels, or nomenclature used to describe or include the processes. For instance, the first two books included in the list below, uses fairly direct words relating to “cleaned and enriched” data in their titles. The other three have less direct words but are extremely helpful and insightful about data enrichment practices. In fact, although item three states techniques in customer relationship management (CRM), I found the majority of references to “customer” to be applicable to “supplier”. Basically, they are both entities that warrant relationship building and understanding.

1. Best Practices in Data Cleaning: A Complete Guide to Everything You...
2. Enterprise Knowledge Management: The Data Quality Approach (The Mor...
3. Data Mining Techniques in CRM: Inside Customer Segmentation
4. Data Visualization: Principles and Practice
5. Big Data: Understanding How Data Powers Big Business

In sum, as implied by the title of this post and as indicated via tweets in Twitter (follow @METAMORF_US, #Analyticship) a forward moving supplier diversity program needs cleaned and enriched small or Big data and the number of sources to collect it, manage it and utilize it has collectively advanced due to the advancement of Business Intelligence and Analytics resources (i.e. people, processes, technologies and facilities).