Off-the-shelf vs Custom Machine Learning Models?
When is building better than buying an off-the-shelf solution? Companies can engage in different approaches to model development. From fully...
Everything you want to do when it comes to analytics – from the advanced stuff, like data science and machine learning, to the basics – hinges on a solid data infrastructure.
In this blog, we provide 7 tips from our experience that will help ensure your data infrastructure supports all of your current and future analytics needs. This is not an exhaustive or sequential list, but rather ideas that we have seen have help our clients.
Before you tackle any kind of BI project, consider questions like: Do you have a data and analytics strategy? What is your company’s overall corporate strategy? What is the business reason behind the need for analytics? You need to define what technology, processes, and people to put in place so you can meet your analytics goals.
Our approach to helping our clients define their data and analytics strategy consists of 4 main steps:
If you don’t have a well-defined strategy, start making one. A couple of approachable things that anyone could start now include:
Learn more about our data strategy services.
This is a given, but without prioritization, your projects may take turns you never intended. Well-communicated priorities help align projects and programs to its strategies.
Use the Prioritization Matrix
Align each of your analytics activities with your overall corporate goals, then determine the technical feasibility of each.
Where within your technology stack do you need the setup of the environment? Consider how you move data through the stack. The whole system will run smoother if this is set up well. Some things you should start documenting when evaluating your environments include:
Ensure your environment is set up thoughtfully.
Read more about our data architecture services.
A data model creates the structure the data lives in, and a thoughtfully created model enables flexibility and ease of use. It also defines how things are labeled and organized which determines how your data can and will be used and ultimately what story that information will tell. Finally, a data model helps define the problem, enabling you to consider different approaches and choose the best one.
Example Data Models
Tools like Qlik, Tableau, PowerBI can help you get better access to your data so you can make better decisions. HOWEVER, if you don’t build a relational data model the solution is not built for the future.
Tools like Qlik, Tableau, PowerBI can help you get better access to your data to better make decisions, but if you don’t build a relational data model, the solution isn’t sustainable.
Why you need a data warehouse:
Use the Bus Matrix. The Bus Matrix contains all of the different core business processes that you’re trying to model along with the common dimensions which is how you will slice the data. It will provide a top-down strategic perspective to ensure data in the data warehouse environment can be integrated across the enterprise, while agile bottom-up delivery occurs by focusing on a single business process at a time.
Bus Matrix Example
This one is boring, but necessary. Without the knowledge of how your data goes from origination to its destination, you could end up rebuilding things later. When you document your data lineage, you’ll be able to:
Build an ETL mapping document. This is a visual of your existing data flow and lineage, including sources and data dependencies, such as revenue. Doing this step during development will save you so much time later on – trust us on this one!
ETL Mapping Document Example
You’ll want to consider performance needs for both front-end user experience and backend infrastructure. Taking time to do this doing the development process will help ensure optimal performance.
Here are some questions you can ask when assessing performance.
Again, start documenting the current state, both the front-end user experience and the backend infrastructure performance. Capture performance benchmarks, assess factors impacting performance, establish SLAs, and identify areas for improvement.
With a properly implemented data governance program, you can gain consistency, get faster time to delivery, lower your maintenance needs, get more quality data, increase user adoption, and a whole lot more. It’s a critical piece to your data and analytics solution, but one that is often overlooked.
We’ve identified 8 steps to implement a Data Governance Program. Read more about these 8 steps.
How to Implement a Data Governance Program
A key point we’d like to highlight: a grass-roots data governance movement will not work. For your data governance program to be successful, you’ll need buy-in from the top and it needs to be championed across the organization. If your team isn’t motivated by the follow the processes laid out, your plan won’t provide its potential benefits.
Starting with the first step, figure out who will be leading the way. You want a leader who looks at data as an asset.