Finding the balance between short- and long-term big data storage

balanced scaleBack before storage became so affordable, cost was the primary factor in determining what data an IT department would store. As George Dyson (author and historian of technology) says, “Big data is what happened when the cost of storing information became less than the cost of making the decision to throw it away.”

But as storage costs have plummeted, data volumes have increased astronomically. I believe that cost is still a consideration for data retention – but risk, productivity, and the analytical purpose and intended use of the data need to come to the forefront of storage considerations.

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Risks to consider when developing a data strategy – Part 1

Creating a strategy for the data in an organization is not a straightforward task. Not only does our business change – our software solutions also change before we can ever get done with a data strategy. So, I choose to understand that a strategy has a vision, and my vision may change over time. There are specific categories involved in any data strategy, and understanding the risks and concerns may help you deal with constant changes in the organization. Some of the categories you should address in a data strategy are data integration and data governance.

Data integration

business meetingI consider data integration the hardest thing to ever complete, for any organization. The reason we never complete it is because we purchase software solutions and then we must feed, love and nurture that software. We create redundant data stores to feed these software solutions, and then we end up creating data stores and data streams to feed other software solutions. (For example, data warehouses, operational data stores and services). To top it off, we buy or get bought by other companies. So data integration has to take on another form. Consider these suggestions:

  • Accept change – it will always happen.
  • Prepare for change – follow best practices in your data strategy. This should include minimizing data redundancy as much as possible, having a vision based on subject areas, and creating a road map that shows new solutions as well as how you will sunset older solutions.

Data governance

Question: Is a data strategy part of data governance, or is data governance a component of data strategy? If data governance encompasses the controls that ensure data quality, that enforce and monitor business rules, and that addresses the integrity of production data, then I consider data governance part of my data strategy. Be aware, though: Data governance needs to start small and embrace change. Start by recognizing how data governance fits into an overall data strategy vision, and off you go!

Find out more about what data strategy can do for your organization in this EIU report.

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How often (and why) does your data strategy need to be updated?

business meetingIn my previous post, I discussed the characteristics of a strong data strategy, the first of which was that a formal, well-defined strategy exists within your organization. This post discusses how often (and why) your organization’s data strategy needs to be updated.

While strategy encompasses and sets the overall direction for tactics and operations, the key difference among them is that strategy must take a longer view and therefore change less frequently. Operations change slowly because they are the day-to-day activities that keep the lights on, so to speak. A lot of data management tasks are operational in nature. Examples include processing daily transactions for an enterprise data warehouse, providing application support to business users, and performing database and server maintenance. Tactics tend to look beyond the day-to-day, targeting near-future objectives, such as the next phase of an ongoing implementation. For example, a master data management system started with the location domain that’s targeting the product domain next, followed by the customer domain. Read More »

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Proposing a business-oriented data strategy

180729711In my two prior posts, I discussed the process of developing a business justification for a data strategy and for assessing an organization's level of maturity with key data management processes and operational procedures. The business justification phase can be used to speculate about the future state of data management required to meet existing and potential future needs. The assessment phase establishes where your organization is in terms of data management practices.

The gaps between where your organization wants to be in the future and where it is today can be formulated into the first cut of a laundry list of desired outcomes. In turn, a data strategy must suggest how introducing new data management practices, improving existing practices, and socializing the necessary changes in the ways that individuals consider, treat and manage data will not only alleviate the noted gaps, but will also deliver quantifiable value. Concretely, this means devising an information environment of the future. That is, an environment with the capacity, performance and functionality to meet existing and anticipated business needs, and to demonstrably lead to value creation or improvement. Correspondingly, the data strategy layers the implementation of data management practices on top of the proposed environment in ways that are directly tied to business objectives. Read More »

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The missing link in your data strategy – Part 2

chain with broken linkIn my last post, we touched on the importance of data migration in an overall data strategy. The reason I wanted to do this is because so many organizations see the migration of data as a technical challenge that can be outsourced and largely ignored by their internal teams. I contend that organizations have witnessed huge failures with data migration projects in the past, largely because they devolve responsibility and ignore the need for a robust approach.

The last article ended with a promise to examine how a typical data migration can feed into your data strategy. I believe there are several critical areas where data migration supports data strategy.

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Assessing your data management capabilities

business meetingIn my last post, I discussed some practical steps you can take to collect the right information for justifying why your business should design and implement a data strategy. Having identified weaknesses in your environment that could impede business success, your next step is to drill down deeper to determine where there may be opportunities to impose best practices in data management. Doing this will require you to assess your current state of data management capabilities and maturity in relation to the capabilities needed to support ongoing business needs.

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Top 5 characteristics of a strong data strategy

With data now impacting nearly every business activity, there should no longer be any doubt that data needs to be managed as a strategic corporate asset. This post examines the top five characteristics of a strong data strategy.


contemplative manAs I previously blogged, in today’s fast-moving business world now often takes priority over later. This means operational and tactical priorities often trump strategy. Some organizations use this as an excuse for why a formal data strategy does not exist. But organizations that are too focused on today cannot capitalize on tomorrow’s opportunities. So the first and foremost characteristic of a strong data strategy is that it exists. Read More »

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Who owns the enterprise data? Part 2: Business

open handWhile setting up meetings with business consumers developing a data warehouse environment, I was involved in some very interesting conversations. Following are some of the assumptions that were made during these conversations, as well as a few observations. To get a well-rounded view of this topic, read my earlier post that focuses on the IT perspective. Read More »

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