IT Governance

5 Steps to Turn Your Company’s Data into Profit

Most organizations believe that information is a valuable commodity, and having as much information as possible can change and drive business success. However, with a vast resource of data comes the need to interpret it into an advantage. In an article for TechRepublic, Alison DeNisco lists five steps to help businesses use their data to get the biggest business impact:

  1. Discovery
  2. Decision analysis
  3. Monetization strategy
  4. Agile analytics
  5. Enablement

Don’t Hoard Aimlessly

If you store information like gathering random garbage on the ground, it will mount up to nothing but junk. Therefore, you need to have strategy and methodology for gathering only items that you need and turning them into business assets. In other words, your data should be something that is able to link decision theory, data science, and agile analytics. In order to do that, you need to discover the opportunities or problems that your organization is facing, and determine what information or kind of actions that are required to improve the situation. This first step helps set the foundation for the rest of the project by promoting an understanding about business objectives and key drivers.

When you know what you need to drive your business, you may feel tempted to make decisions as quickly as possible. But good decisions require considering factors such as team biases, most notably confirmation bias in which a team has already decided upon something and seeks for information to support it. Remember that information is best used as a guide to help you seek the best decision, not support what you already have in your mind. A good decision should create value that benefits the business and impacts overall business performance.

DeNisco says that using agile analytics also allows a company to enable new decisions and create a solution:

This system means that every manager in the organization can access a rich set of data at its most granular form, as opposed to using reporting systems that generate reports based on averages and aggregations and lose insights. “Data modeling becomes critical—where before you had daily summaries that were simple and flattened, now, marketing, business, and IT analysts will be able to work with a rich spectrum of data,” Chiang said.

Don’t forget that your data needs to be updated and valid with correct calculations. Users also need to feel invested in it. It’s ironic if you have excellent analytic solutions but your users just cannot trust your data. If they are not bought in along the way, it’s hard to create value for what you’re doing.

You can view the original article here:

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