Once you have defined your data strategy and data hosting, you can begin to align your goals with your data-driven strategy plans. This alignment must be done across the organisation; there is no room for individual business needs to determine whether a certain data storage goal is feasible.
Lack of a data strategy can be very costly. Conflicting business needs, going a different direction, or falling behind could result in significant financial losses. Companies will find it much easier to move toward the agreed upon data-driven strategy objectives if they are able to clearly define their goals and understand that other departments must be able to align with the goals or make exceptions. This article will cover five steps to develop a data strategy.
Step 1: Understand the real value of your data business. First, you must be able to understand the cost of not having your data in house, as opposed to the cost of having a data infrastructure that you host away from your data-driven strategy company. Cost is just one factor in the equation, however, the highest cost to your company can be related to the following hidden costs:
For example, a company's data warehouse can have to maintain multiple data integrity concerns. This is especially true if the data is held as a local appliance in your own office instead of a remote service from the cloud. Building this type of application into your standard database can be expensive and inefficient data-driven strategy If you have not built into this data solution your own database infrastructure.
Step 2: Assess your existing data storage plan. What do you currently have in place? What are your current data-management practices? Do you know if you are paying too much for your data?
Step 3: Identify necessary data processes. There are several critical factors that must be considered before choosing the data process that is best for your organisation. These include:
There are many other data-driven strategy concerns that will affect the solution that you ultimately choose to implement, however, these are the most important for this post.
Step 4: Create a 'best practices' matrix. In order to find the best solutions, there must be a standard across the entire organisation that can be applied; broken from this standard to prevent a burden of one data-driven strategy department to another, for example, is a well-known framework that many companies implement in order to find the most efficient ways to store their data. In this way as you identify the data processes with the most efficiency, other questions can be asked to identify other process areas that might be improved.
Step 5: Generate a ranking matrix. What processes from step four come to mind as the most effective? What will you base your ranking on? These are the questions that you will need to answer before you are able to generate an effective solution.
Enron's 'waterlogged' information was based on more than a 5-year old whim; the round-robin has proven to be a superior model of evaluation and limits the #of levels to 5 for data.
Step 6: Validate your existing solution. Once you have narrowed down and eliminated the process that you believe is the best data-driven strategy based on the seven key steps, you may decide to implement the solution, it will be advantageous if you can validate your choice. You can use the scoring matrix to determine the 'best' solution.
Step 7: Resolve to get into a continually evolving state. As you continually expose new issues and applications that come along the way, you will be forced to continually expose your current process, thus requiring that you are prepared to change this. In order to meet these new challenges, make sure to look for a data-driven strategy service that can offer you a practice improvement kit and real-time analytics.
What are the disasters that have plagued you? Disasters can affect your company and its bottom line extremely negatively. For example, if your company goes into a bind and cleans up a predictive component of its data and the results are interiorized too late, you could be faced with significant jeopardization. You also need to consider which disasters can occur that can have a direct impact upon the overall profitability. (A disaster can cause you to look at your data more closely than you currently are.)
How long do each of these disasters last? With stability and long-term decisions important in each of the areas defined above, you need to determine for each of these disasters the immediate impact and the negative outcomes they can cause. Will these disasters affect your ability to meet today's' data-driven strategy business climate? Does your solution implement effective measures for the future that will enable you to make any necessary adjustments?