Data, Trust & Transformation: The story of gather360’s development
In the nine years since we started Think Evolve Solve, we’ve assisted enterprises across a variety of industries. Our team of engineers, mathematicians, developers and entrepreneurs has implemented a broad spectrum of solutions, from data strategy implementation to standing up advanced ML initiatives.
No matter the solution, there has typically been one overarching reason why each organisation is looking to improve their data processes: digital transformation. The first step on our journey with a client is to share our unique perspective on how to put data at the core of this transformation.
Most organisations looking at digital transformation start with the typical methodology of people, process, technology – by listing their assets in each area to understand how they need to transform.
This method has one flaw – it’s missing a view of the data assets. In today’s world, an organisation’s data is as valuable as the other assets listed above but often isn’t part of the initial considerations in digital transformation.
With today’s unparalleled capability to collect data, successful organisations should be using the knowledge they have at their disposal to drive and inform the decisions that impact how we use the other vital assets in our business (People, Processes and Technology).
Data, Process, Technology and People.
We present an alternative methodology to the companies we work with, one that includes data as a critical building block in an organisation. This methodology delivers significant benefits by introducing an understanding of data assets and their contribution to the business model overall.
We encourage companies to consider data first: enable your business team to identify and categorise all the data assets required to run your business without confusing or constraining this view by the technology or processes that generate your data. In this step, you are creating a framework of the data assets in your organisation.
This is a great educational exercise for your teams, who will begin to consider data as an asset that drives performance in its own right and to identify risks and opportunities in your data landscape.
Once your data framework is complete, it’s time to consider the existing processes and technology in your business. The processes and technology may inform and expand your list of data assets and identify additional opportunities to capitalise on if you collected or stored data differently.
The resulting list of existing and potential data in your organisation will form the core of your operating model going forward by characterising the information you have to hand to influence decision-making.
Getting the most from your data.
Data is the fuel of modern business. Whether it’s a CRM system, product inventory, historical trend data or sales & accounting information, the data you collect will oil the cogs of process to drive efficiency and inform the reports on your dashboard to drive business insights.
We’ve learned from the world of motoring that the quality of this fuel is essential. It’s what determines whether you’re in the pitstop or flying down the highway.
Effective quality assurance usually comes down to your ability to assess four things:
- Accuracy: Is the data clean, valid, and correct?
- Timeliness: When was the data received/generated?
- History: Where did the data come from? How has it been quality assured and edited?
- Access: Who can view or edit this data?
One of the easiest and most efficient ways to monitor and manage these four data characteristics is to store your data in a central, accurate repository that we call a ‘quality data layer’.
What is a quality data layer?
A quality data layer is a central, clean repository of all the data assets in your business. This repository should act as the ‘source of truth’, storing data in a consistent, consolidated format ready for utilisation by your systems, processes and reports.
A quality data layer is read-only, so it can’t be adjusted or amended to suit a single business need. It must have a system of governance to ensure that all data that enters the quality data layer is validated and tested to ensure it is correct and accurate. Information in your quality data layer should also have a level of transparency, where essential information like the data source, time of creation, validation rules and change history are stored for users to audit in the future if required.
Them: Can you just quickly pull this data for me?— Seth Rosen 🇺🇸 (@sethrosen) April 20, 2020
Me: Sure, let me just:
SELECT * FROM some_ideal_clean_and_pristine.table_that_you_think_exists
Superfuel for your business.
Once your quality data layer is in place, you have a single source of truth within your organisation. Having access to clean, trustworthy data will significantly reduce the effort spent cleaning and reformatting data by your business teams.
In a company without a quality data layer, it’s common for teams to spend time reformatting data for system needs, copy-pasting data into a single, consolidated spreadsheet, or even just trying to understand where the data they are looking at has come from.
We eliminate this effort by having a quality data layer, freeing your data analysts to focus on driving insights, not cleaning data.
How does this relate to gather360?
Think Evolve Solve has worked on a wide range of data solutions. Still, no matter what size of organisation we are working with, we regularly correct data quality first. We’ve implemented data quality layers in various organisations and industries, and it always has a revolutionary effect on data utilisation.
As a result, we made it our mission to create a simple, fast, and effective way to create a data quality layer that works for business teams, operations teams, and technology teams.
We wanted to make it easier to identify data assets and maintain the quality of those data assets. So, we built in automated validation. And to support the creation of a single, consolidated data layer, we added automated transformation, consolidation, and enrichment. Finally, to evidence the data quality, we built an audit trail to verify and trace the history of data in a quality data layer.
As the product has evolved, we’ve added a whole range of ways to connect data from your data quality layer to your systems and processes using gather360. We’ve also added the ability to manage, monitor and identify bottlenecks in your workflow with our data ops dashboard.
You can find out more about gather360 here, or contact me directly to arrange a time to chat further on its applications. If you’d like to discuss how a quality data layer could help your organisation, I’d also be happy to talk on LinkedIn.