Umid Akhmedov is head of data and AI CSA at Microsoft and was formerly the head of architecture and data & analytics at FLSmidth
As a data & analytics leader, I like to create simple goals that anchor data & analytics in tangible business results. While these goals may vary from industry to industry, your topline goals are probably similar to mine:
- Create a data-driven enterprise
- Turn data assets into revenue generating resources
Of course, as anyone responsible for data & analytics understands, achieving these goals isn’t as simple as it sounds. Huge volumes of data and complex data environments present significant roadblocks.
That volume and complexity can be made even more difficult by the size and history of your organization. For example, FLSmidth is a multinational engineering company based in Denmark with nearly 12,000 employees worldwide. The company has been growing for more than 130 years with numerous acquisitions. Every time a new company is acquired new systems are brought in, new data assets are added that aren’t available to everyone who might need them, and there is a lot of tribal knowledge that gets lost when people leave the company.
To enable data & analytics, first you have to untangle all of that complexity. A data catalog can help immensely by enabling faster data discovery, centralizing documentation on data, and fostering the kind of collaboration that leads to greater productivity. The problem is that while a data catalog solves for some of the biggest pain points in data & analytics, few of these pain points on their own make the business case for buying and implementing a data catalog.
Making the case for a data catalog more often than not rests on your ability to connect the data catalog to tangible business value. In my experience, that means finding concrete use cases that demonstrate how the data catalog can improve the bottom line. I’d like to share a couple of those examples in this blog, and hopefully, give you a better understanding of how to make the business case for a data catalog.
Predicting Customer Needs
As an engineering company that provides global cement and mineral industries with factories, machinery, services, and expertise, FLSmidth not only makes money with plant sales but with aftermarket parts sales as well. From the time a plant customer makes an order for a spare part, it can take weeks to go from the quoting, ordering, handling, and delivery to getting back up and running. While the customer waits for weeks for their part to arrive, they are losing production time, which means they are losing money. And because of that delay, the customer may turn to local smith, and we miss out on the sale.
Rather than forcing the customer to go through this costly delay or risk losing the sale, we can leverage predictive analytics to identify when a part will fail 90-days beforehand and can proactively inform the customer when they should order the part to experience the least amount of negative impact.
In order to conduct that kind of predictive analytics, we need to understand exactly what data we have and where we can find it — and that’s exactly what a data catalog can help us do.
Sell with a 100% Chance
Another example relates to the kinds of calculations we can do to improve our ability to sell products.
We evaluated inventory in our warehouses and sales data, and found that most of the parts we have in stock were used by one customer, and the parts we have in stock weren’t actually the parts that end up selling. If we can identify and stock the parts that are actually needed more often, we will create a much higher probability of selling that stock of spare parts. Optimizing just this part of our business is significant enough to increase our sales by up to 4% and help keep our customers’ plants operating.
This example demonstrates the impact that data & analytics can have on the business, but it only works when the calculations are based on trusted data.
The kicker here is that we were only able to create these calculations on 60% of our transactions. While the potential impact was huge, the trust in the calculation wasn’t there because our leadership wasn’t convinced that we had the right data. This is where a data catalog comes in.
With a data catalog, we can ensure that we are using the right data, and in turn, our leaders can be confident that we are making these business critical calculations on trusted data. This is how I was able to make a clear business case for a data catalog by speaking the language of our company’s leadership.
Choosing Alation’s Data Catalog
We did an extensive search for the right data catalog including all of the major vendors in the space, like Collibra and Informatica, and open-source solutions.
When compared to other vendors in the space, we really liked Alation’s collaborative approach. The way Alation is designed makes it easy for people to find the experts, discuss things related to the data, and contribute to documenting information about the data as a team — rather than tucking important information away in an Excel sheet somewhere. That collaboration extends to data governance. With Alation, data governance becomes part of the way you work with data. You can immediately see what guidelines or policies are applicable to the data set, who the owner is, identify the steward who will give you access, or who to contact if you have questions.
At a previous company, we had tried to implement an open-source data catalog and there are notable shortcomings when compared to Alation. Open-source applications don’t crawl the data automatically. Without the machine learning that is in Alation, there is a large burden to make constant manual updates to the data catalog. The open-source data catalog also requires a dedicated development resource to maintain and had to be run on-premises. For all of these reasons, Alation was the clear choice for helping us reach our business goals.
Speaking the Language of the Business
As a data & analytics leader, it is easy to get fixated on the blockers right in front of you. My teams can’t find data — I need easier search & discovery. My teams can’t understand how to use the data — I need a business glossary and better documentation. My teams are working in silos — I need to find ways to help them collaborate. But while these are clear pain points that need to be solved for, they don’t clearly connect to creating business value. For anyone who realizes that they need a data catalog but are struggling to make the business case, I recommend drilling into the tangible use cases to illustrate exactly how a data catalog can improve the bottom line.