Data Strategy in Detail: Faster Decisions


Today we pick up where we left off last week talking about data strategies. Last week we focused on what a data strategy was, how to develop one, and why it was so important to providing a clear and logical path to accomplish the business goals. This week we are going to focus on different types of data strategies. 

Today its all abut being faster. The way that business moves, you have to be faster than your competition.  You have to get that service ready for market first, or that product ready to ship.  Have you ever wondered what the difference is between the two organizations, the one that gets to market first vs the one that follows?  The short answer is the influencers in that winning organization made the decisions first with confidence.  And how did they do that?  They had a solid data strategy behind them that enabled them to be fast.  With the proper information that is easily accessible, a businesses can take a good "gut" decision and turn it into an actionable path.

The business outcome we need is: Faster decisions with a high level of confidence. We know we need the data, it can provide insights into customer behavior, market trends, and other factors that can impact business performance. However, data is only valuable if it can be easily accessed and analyzed.

Here is where a well-defined data strategy comes in. A data strategy focused on collecting, storing, managing, and using data to make good decisions.

What would a successful data strategy focused on faster decisions do?

  • Eliminating data silos: Data silos are a major obstacle to data-driven decision-making. By breaking down data silos, organizations can create a single, unified view of their data, which can be used to make better decisions.
  • Enabling real-time analytics: Real-time analytics allows organizations to make decisions based on the latest data. This can be essential for businesses that operate in fast-paced industries, such as retail and finance.
  • Automating decision-making: In some cases, it is possible to automate decision-making based on data. This can free up human resources to focus on other tasks and can help to improve decision-making accuracy.

That is all good and well but how does an organization put a plan like this into action? Here are some specific steps that can be used to accelerate digital transformation and enable faster decisions:

  • Invest in data infrastructure: This includes investing in data storage, data processing, and data analytics tools.
  • Create a data-literate culture: This means ensuring that everyone in the organization understands the importance of data and how to use it effectively.
  • Set clear data governance policies: These policies should define how data is collected, stored, managed, and used.
  • Prioritize data quality: This means ensuring that data is accurate, complete, and consistent.
  • Use data visualization tools: These tools can help to make data more accessible and understandable.
  • Adopt an agile approach to data analytics: This means being willing to experiment and iterate on data-driven solutions.
If you can get your organization to start down the road using this specific data strategy, over time you will see your leaders make faster decisions. They will start small but after they see success they will start to rely more and more on your data. Soon enough you will see the big decisions getting made at the speed of business.

Want some more Pro tips? When you start with this data strategy also keep these things in mind:

  • Start with a clear understanding of your business goals. I cannot say this enough. Communicate what you want to achieve by using data. Once you know your goals, you can develop a data strategy that is aligned with them.
  • Involve all stakeholders in the development of the data strategy. This will ensure that everyone is on board and that the strategy is implemented effectively.
  • Regularly review and update the data strategy as your business needs change. Data is constantly evolving, so it's important to make sure that your data strategy is keeping up.

Comments

Post a Comment

Popular posts from this blog

Data Strategy: Guiding Principles

Image
  It has been a long time since my last post, and a lot of things have changed.  I have a new job, I have moved to the other side of the state, and I even have a new dog.  But one thing that has stayed with me is my focus on data and how much potential it has.  All of this change has also solidified my understanding of why a data strategy is critical to any success in the data world.  Without a strategy, your effort will be as useless as trying to strike the wind.  You have to be able to aim your blows if you intend to hit anything.  In our case, we want to "hit" that business objective, the one thing that the business must have to progress to the next phase in its development.   The thing I have come to understand is that when the term "Data Strategy" is used, most people in management jump right to the end and focus on things like cost or "vendor lock-in".  Some people in management focus so much on what they perceive the outcome will ...
Read more

Data Principles: The Power of Naming Standards

Image
In my last blog, I highlighted five data principles that I have used in the past to create a solid data strategy.  The idea behind a principle is that it is flexible to adjust to changing needs but rigid enough to maintain order.   A good example of this is the golden rule, "do unto others as you would have them do to you".  When you apply this principle, you are free to do whatever you want, as long as it is something you want done to yourself.  It is a very flexible principle that can have serious consequences if broken.   One crucial principle that forms a solid foundation for your data initiatives is  naming standards . It might seem like a simple concept, but consistent naming conventions throughout your data environment can significantly impact efficiency, governance, and understanding for everyone involved.  With the right naming standards, looking into the data environment as a DBA, developer, or user, there will be no question about ...
Read more

Data Governance: Check and Check Again

Image
We are progressing in our data governance by building out the framework.  We are moving data into areas that the business can use it but how do we know they should use it?  More importantly how do they know that they CAN use it? So much of what we do relies on trust.  The business trust that we will understand what they need so that we can deliver reliable data.  The first time they find an issue that trust is broken.  Part of our framework builds out data validation processes but how do we put that into practice? One of the first things we will need to do is check ourselves. Y our data governance framework can be used to create natural data quality checkpoints. Is your data pipeline ready to migrate? Did you check it? When your process captured the requirements did you ask who in the business would validate the work? Your processes are the first place to start checking your data. Some specific examples of how you can use your data governance framework to cr...
Read more