Why digital business needs a trusted data foundation
This is an era in which everything has become digital. We live in the data-driven society.
The difficulty is that all this digital technology still doesn’t totally cooperate.
This is the first blog post in a series zooming into the full data-management lifecycle. I will show you some simple steps you can take to build a solid, trusted data foundation that brings value to data and helps you make an impact as a data-driven organization.
Data needs trust, like a best friend
Everything is digital these days, but the world still runs on people. How do organizations achieve big results? It’s all about trust.
Trust is the foundation of all relationships. You likely know when another person has the capabilities to help you solve a problem or brings value to your relationship. Clear agreements and facts that prove that person indeed has the abilities to achieve a task. Building a community fueled by trust comes naturally to people.
As culture becomes more data-driven, however, our basic instincts need to be reframed to effectively make use of technology. Techopedia defines being data-driven as being compelled by data, rather than by intuition or by personal experience. No longer can people solely rely on gut feelings to be successful business decision makers.
The flow of information coming and going not only within organizations, but between individuals, is immense and variable. While technology is here to help, people need to do the legwork to ensure data can be trusted, especially with analytics, artificial intelligence (AI), and the Internet of Things (IoT). Technology now enables people to make a digital copy of everything they do:
- Every move you make is registered by your mobile phone.
- All the places you have visited are maintained by mapping applications.
- All the images you take are tagged with their location.
- Artificial intelligence is getting better at understanding the who, what and where on every image and video.
- Security cameras register every movement on the street and in buildings.
- All your online behavior is registered somewhere, capturing a detailed history.
This vast amount of information is of a complexity and volume that it must be managed if you want to glean the correct insights.
Know where you want to go
Don’t try to start with all your data management initiatives at the same time.
First, find out what is missing or where your data initiatives are experiencing the most pain. This will help you define which angles of data management you should prioritize. Some of the top questions include:
- Data Provenance (“Where does my data come from?”)
- Semantics (“Is a marketing customer the same as the supply chain customer?”)
- Data quality (“Which axes are best for measurement?”)
- Technical integration and metadata
- Policies and data governance (“Who can do what in which situation?”)
- Data modeling (“Conceptual, logical or physical?”)
Recently I participated in an agile workshop in which we were tasked with building a LEGO house. All the participating teams had the same guidelines and requirements: two windows, a door, a roof, and four rooms. Not long into the building process, it became clear that without a unified definition of what we wanted to achieve by building the house, my team was working to meet individual needs.
Similarly, when you begin your data journey, alignment across teams, departments, business units, and industries is critical. You must start by determining the value of what you have to build. This applies to data warehouses, data lakes, analytical models, or any architecture you are approaching. The building blocks should be clear before you start building.
Once you know why and how you are working toward your goal, you can start identifying your resources to make it happen. Whether you are using bricks or data, the first step is to clean, classify, sort and qualify what you have to ensure only the good pieces make it to the next step. This will enable you to effectively gain benefits down the road.
Know your data
When it is time to start, break it down into simple steps:
- What is it? Identify the concept, entity and dimension.
- Write it down. Describe the concept or entity and how it connects to other concepts.
- Model. Draw a diagram that represents the concept or entity within the organization, with all its interactions. Pin down a view and go. You don’t need perfection.
- Bring the real data to the table. Get a better understanding of all the properties that define your concept or entity, as well as the complexity you face in defining the concept.
- Visualize. Make the drawing of the model visible. It will start the discussion then encourage refinement and agreement.
- Discuss. Bring more insights to the table and include all appropriate teams.
- Iterate. Repeat the exercise until you have a working, fit-for-business definition and diagram of the concept.
Build once, use again and again
Taking the time to agree on a business vocabulary that will be standard across the organization and in various contexts, such as industry, checks a major step off the to-do list. It means you don’t have to keep repeating the exercise every time a project surfaces. Once you build the trusted foundation, you can move quickly and avoid ambiguity.
By taking small steps in your data journey, you’ll get a better understanding of the potential of your data, the issues and the complexity as you go. This furthers insights which help you better define upcoming priorities, bringing agility to your business strategy.
Keep following this series for more simple steps you can start taking to build your trusted analytics foundation. In the meantime, read the survey results on the top challenges businesses are facing this year when it comes to governance.