SAG_Twitter_MEME_Cure_Big_Jun17.jpgAs they say, be careful what you wish for. Transforming your organization into a complete, digital enterprise is really just the beginning. 

Feel free to check with any of your forward-leaning trading partners and you will hear that with complete digitalization comes Big Data, big challenges and maybe even bigger headaches

Inconsistent customer data and addresses, untrusted business intelligence, rejected compliance reporting and SEC filings, redundant product catalogue entries, inaccurate inventory—it turns out that even the best-run digital transformations initiatives need to deploy data management strategies. They need to not only to maintain control over increasing data volumes, but also to help maximize data’s big business value.

At a high level here are five different, but complementary, recommendations that should help dispel the I.T. fog currently obfuscating how you can best extract true value from your data.

  1. Streamline IT Systems

Often associated with M&A activity, reducing system redundancy and waste is key. As enterprise digitalization progresses, you need to maintain an inventory of IT assets, encourage portfolio management, document stakeholders, dependencies, focus on deprecation and retiring applications. The objective is to create an infrastructure that eliminates data silos, identifies the system(s) of record – and ultimately lays the groundwork for consolidation of disparate versions of master data.

  1. Integrate On-premises/Cloud Computing

Streamlining IT and reducing systems and application redundancy paves the way for system, application and data integration.  A common, API-powered integration platform will connect all your diverse applications to communicate with each other, as well as allowing data to be easily leveraged by both end-users and developers.  If you don’t have an Enterprise Service Bus (ESB) framework in place, start considering one right away to help manage disruption. Eliminating complex, point-to-point connections enables quicker consumption of application data and reduced time-to-market for new applications - both on-site and in the cloud. 

  1. Canonical Modeling

Consider canonical modeling as part of an executable form of data governance. This form of data modeling provides additional enablers for application integration by linking different data formats.  Canonical structures are designed to standardize on key shared business attributes whether they appear in ERP, CRM or other best-in-breed solutions.  Therefore, it promotes data accuracy by standardizing on agreed data definitions. This approach is often adopted for implementations of Master Data Management (MDM) system and repository.

  1. Analytics

Today’s analytics are not your grandfather’s, data-at-rest business intelligence reports (not that there’s anything wrong with that).  But predictive, streaming analytics and operational intelligence in general, are the new-norm when it comes to extracting valuable, real-time insights from your data. 

Additionally, companies are relying on the intrinsic value of their data to manage continuous improvement and transformation projects.

  1. Data Quality Management

Unfortunately, all of the above recommended strategies can be undermined by preexisting and intrinsically poor data quality.  Redundant, inaccurate, incomplete and, of course, duplicate master or reference data is guaranteed to insidiously compromise the best IT efforts, analytics and BI reporting.  Certainly the trust in analytics suffers – but even a strategy as seemingly solid as application integration is a concern since application data may be inherently inferior.

No wonder MDM has been adopted to create a baseline of good and consistent data. With its data consolidation and data quality capabilities that help synchronize any enterprise data, the creation of MDM’s single-view-of truth is often embraced as the first implementation step for digital transformation projects.  It maximizes your data’s value while curing the headaches.

Cure your Data Headache here

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