Source:- LinkedIn Author Jay Zaidi
Data Management tools (e.g. Data Governance, Data Quality, Metadata Management, Master Data Management, Business Intelligence, Analytics, Information Security etc.) play a critical role in Strategic Data Management related activities. In this post, I will address the top 5 common misperceptions that end-users and business leaders have about them.
We are familiar with the 3 pillars of management – People, Process and Technology. I would like to add a 4th pillar – Data. Data Management tools only address 2 of the 4 pillars – Technology and Process. The other two pillars – People and Data aren’t addressed by them.
There is a common misperception among leaders that data management tools will solve all their organisation’s data problems. Unfortunately, this isn’t true. Tools are enablers and accelerators, but will not solve business problems – if there is poor management, teams don’t leverage the tool, there is a lack of skilled data management staff, the tools aren’t integrated and aren’t customised for use.
The simple example I use is that of an auto mechanic. A mechanic is a skilled technician that specialises in certain makes and models of automobiles and uses various tools such as wrenches, spanners and data analysers, to get the job done. Tools are enablers and accelerators, but aren’t necessarily able to fix the issues. Its the combination of skilled resources and specialised tools that are required to solve problems. The same analogy applies to data management tools.
Data management tools do require configuration and customisation, to meet an organisation’s specific requirements. Product experts and tool specialists are needed to setup the tools, so that they get the best value from them.
This depends on the specific tool and its usage. A data quality tool for example, will pay dividends on Day 1, since it enables organisations to automate data profiling, rule development and data quality reporting out-of-the-box. On the other hand, a Metadata or Context Management tool will require significant upfront effort to configure, upload content and keep the content current. Given the massive amount of contextual data that organisations store, there is ongoing investment and governance required.
The bottom line is that tools enable organisations to automate many of the manual processes. They can also provide valuable insights, enable data discovery and perform other functions, which results in a significant return-on-investment. However, there are others that will require ongoing investment and may not necessarily reduce expenses.
This specific topic relates to build vs. buy decisions. Some organisations prefer to build and use home grown tools. They are able to customise the tool(s) to their specific requirements and remove dependencies on third party vendors for support. This is a great option for organisations whose needs aren’t satisfied by commercial products or for organisation’s that can afford to develop, support and maintain a custom tool.
With software-as-a-service offerings in the Cloud, commercial-off-the-shelf applications are becoming far more attractive. It is advisable for organisations to move to a buy model, rather than a build model, if there aren’t compelling reasons to build a tool.
Data management tools can certainly automate some of the processes, but may not be able to completely automate all processes. A significant amount of integration and workflow automation would be required to enable automation across tools and applications.
Data Management tools should only be considered enablers and accelerators. They aren’t “Silver Bullets” to an organisation’s Data Management challenges.
Organisations need a combination of a Data Management Strategy, Execution Roadmap, Data Management Frameworks, skilled Data Management resources, appropriate Governance and Controls, and Data Management tools, to succeed.
Go forth and conquer!