Enterprise Data Warehouse and Enterprise Data Strategies

Any organization that uses data need and stores their data in an Enterprise Data Warehouse needs an enterprise data strategy. An enterprise data strategy is a plan and vision that an organization has that covers the organization’s ability to harness and manage its data and data capabilities the right way. There are different types of enterprise data strategies and there are some that are more common than others. Some of the most common and frequently used enterprise data strategies are master data management, big data management, and business intelligence. Any strategy that is domain specific and uses data can be classified as an enterprise data strategy. However, for a strategy to be qualified is good it must have the following qualities

 

Xteristics of a Good Enterprise Strategy

 

  • It should be practical and easy for the organization to follow during the day to day operations
  • It should be built within the context of the organization in mind, therefore, it should be relevant and useful to that specific organization
  • It should be evolutionary i.e it should be changed and improved upon regularly
  • It should be connected and integrated with other systems that exist at the same time is it does and those that will come after

Why an Enterprise Data Warehouse needs a Strategy

There are key reasons why organizations with enterprise data warehouse need a strategy. Let’s discuss some of them below

 

  • Any organization considering using big data needs a strategy. Big data is so endorsed and ever growing that going in blind will lead to a chaotic experience so any organization that hopes to have positive impacts on their EDW needs a strategy.

 

  • A strategy helps set priorities with other existing data sources. Before implementing and even designing a new strategy, it is important to collect an inventory of all the current data sources an organization has is well is other existing applications and data owners. This will help the organization understand the scope and complexity of their current data and incoming data and thus aid in making better decisions. It like taking a stock of the food you have in the pantry before making a grocery list and going grocery shopping.

  • A strategy will help the organization better understand the logic of it all. In an organization there is usually a misunderstanding between the business (sales) and technical department is each tries to explain why their needs are more important than the other. A strategy acknowledges this misunderstanding and helps rationalize it, giving each side a logical understanding of what’s at stake.

 

  • A strategy also deals with how to get rid of the old system. These older systems (also called legacy systems) are usually so ingrained in the organization that introducing new ways is often met with hostility and an unwillingness to participate or corporate by employees. An enterprise data strategy can use the inventory to develop a roadmap and strategy for modernizing to anticipate new big data sources and desired analytics capabilities.

 

  • A strategy improves the quality of the data and the data process. This goes without saying but anything that’s planned and appropriately strategize always has a better quality than one that wasn’t. It also means reduced inconsistencies, redundancies, and gaps in data.

 

  • New data always bring value to an organization but it also brings with it some risk. A strategy requires you to think about the potential values and risks and weigh the potential benefits and problems that might come with it. It’s better to access the risk that your data might expose you to before you let the data in

  • Working through an enterprise data strategy should make your enterprise more aware of the total amount of data collected and stored. understanding how much data persists in different applications and determining how long the data is considered viable helps avoid any potential burden or unnecessary costs.

 

  • An enterprise data strategy establishes decision-making authority for data governance and data management. In an organization, you need to state expressively, who is in charge of the big data, what is the decisions making hierarchy and who reports to who. Before bringing it in, the current hierarchy system has to be examined and understood (maybe make a process map)that way this old hierarchy would be absorbed into the new system in a way that makes integration a lot more easier. Think of it is matching people into new systems that they are comfortable with because it mirrors their old task and so this way they are more efficient and employee morale is kept at a high level.

 

  • A strategy clearly states the benefits of big data and how it will benefit and enrich the existing system. When an organization has a robust enterprise data strategy for the current state of affairs, they can begin to plan for where they should introduce big data sources to supplement analytics capabilities versus where they would introduce risk

 

If you are in charge of a  large organization or if you own a startup company and you’ve decided to grow your business with the support of big data, it is important that you put together a team of experts to help you do this properly. This is because data science is a team sport and is no job for just one person. There various branches and nuances that can be missed if one does not plan it properly and use the right type of resources. The planning sometimes takes just as long as the use. But in the end, it will be all worth it is the advantages that come from using big data are great.