The pace of new data creation has grown exponentially in recent years with more information available via the cloud, mobile devices, social media and integrated solutions. How exciting it would be to include that data in your organizational decision-making and processes! Data Management software and tools enhance your overall strategic vision, but they are only an enabling force that can determine the success of failure of your strategy.

The closer aligned your data management model is with your overall organization strategy, the better positioned you are to leverage your data for competitive and operational advantage. Evaluate your data management scope, policies and processes across these five dimensions for a well-rounded approach. Each dimension affects not only your internal operations and performance! It also directly impacts your customer relationships and service delivery.

Scope of your data landscape – Information is everywhere these days, and data integration tools are making it easier to enrich analytics models and enhance decision-making. Take care to make balanced decisions about which data is most valuable to your organization, however, because each data source is a data management investment long after it is initially added to your data portfolio. Too much data can actually be a hindrance! Things to consider:

  • How does this data contribute to our strategic objectives? Will this data help us differentiate our offerings, lower the bottom line or meet a specific customer need?
  • What is the financial value of the data source? Will the impact exceed the implementation and management cost? Is the organization ready to assimilate this data now?
  • How critical is this data? Does it drive real-time decision-making or does it support historical and/or predictive modeling?
  • What volume of data is needed to optimize value? Can the data source be summarized? What volume of historical data is needed?

Regulatory Requirements – Legal and governmental rules, policies and laws drive industry-specific needs. A smart data management plan can reduce the time and cost to meet specific data retention, customer privacy, reporting and business continuity requirements. Things to consider:

  • Does the data need to be immediately accessible? Will historically compiled results and reports be sufficient to meet most requirements?
  • What are the confidentiality and privacy requirements? Who has access to secure data?
  • Are there rules about storing historical data? How long do you want or need to keep data available?

Business Continuity and Risk – Whether the issue is intermittent access interruptions and delays or a major system outage, hiccups in data availability can have major impacts. As databases and analytics tools become more accessible to end users and analytics engines provide more immediate results, the impacts of outages start to resonate further and further down the value chain. Evaluate the value of data and the impact of a lack of that data on your processes, employees and customers. Focus your energies and resources on continuity and disaster recovery for your most critical data and develop a tiered approach for less essential data. Things to consider:

  • How will a loss of this data impact the organization? How long can we operate without this data?
  • Is the data used in real-time, ad hoc decision making? Will an outage directly impact customer, supplier and employee outcomes?
  • Is there an alternate source for the same or similar data?
  • For external data sources, what will happen if the information is not available? How long until it impacts the organization?

Pace of Decision-Making – The latest data management and analytics tools have shifted decision-making to the front line. Faster and more robust access to useful information drives dynamic pricing decisions, purchase decisions and supply chain management. Differentiate the systems and data sources needed to support your organization’s transactional processes from those used for offline analytics and reporting. Things to consider:

  • How could real time analytics change your business? What kinds of information are critical and at what point in the value chain are they most useful?
  • How “fresh” does the data need to be? When is the data too old or outdated to provide value?
  • Is there any information or insight that could deliver game changing value? Is there a way to source this data?

Lifecycle of your data – Without a data lifecycle plan, you will have ever-growing databases and an ever-widening scope of data options. Not only is this glut of data confusing, it also comes with a lot of overhead and management costs, such as storage space, data governance and IT management. To avoid spending time and energy on things that no longer have a strong ROI, determine the expected lifecycle as you add data sources to your model and manage out expired or no longer useful content. Things to consider:

  • What is the timeframe of usefulness for this data? Does it support transactional processes? Will it contribute measurably to historical reporting, predictive analysis and/or enterprise analytics?
  • Is there a way to summarize the data to reduce overhead while preserving the value?
  • Who used this data and how is it critical? Is there another data source with similar value and application?
  • Are there any legal data storage requirements? How long does the data need to be available (and how available does it need to be)?

Dell Data Management Solutions

Dell was recently lauded for its novel approach to building and recommending enterprise IT solutions to customers by CIO Magazine. By focusing on “What problem do you want to solve?" - as opposed to hardware or software product or packaged service – Dell can deliver the best possible solution for your needs.

Learn more about Dell’s end-to-end data management solutions.