Data Strategy

In recent times, our clients have increasingly sought our assistance in shaping their data strategy. Various factors, including the proliferation of SaaS tools in the ecosystem, the vast and diverse data landscape, the heightened significance of data, the influence of AI news cycles and the latest developments in machine learning drive this surge in interest. While data strategy is not new, the landscape in which it exists has changed significantly.

SaaS Landscape

With the expansion of the SaaS ecosystem, there are more tools than ever catering to almost every niche. SaaS products and data residency have become more prevalent, and discussions around these topics have also increased. Although, systems integration has been around for a long time (we’ve done plenty of it).  Back in the day, with fewer tools in the ecosystem, most of the data would be in core operational or accounting systems. Accordingly, there were associated data integrations, but they were usually point-to-point. Move the accounts from the accounting system to the operational system, so you can create work orders, and then move the work order back to accounting for invoicing.

Competing desire for data products and insights has changed the integration conversation to be more data-centric. This has resulted in a convergence to more holistic data solutions, rather than point-to-point integrations. The challenge has become a lot more strategic than tactical. Point-to-point integrations were tactical, and integrations & data products have made the conversation strategic. If you can stage data as you integrate it, then you can also use it in other ways.  

This also affects the procurement process. If you buy a SaaS product, how easily is your data available to you? What happens when you want to change tools? What happens when your business makes an acquisition? The increased SaaS choice in the marketplace also gives people optionality. Don’t like the work order functionality in your current operational tool? The good news is that there are a hundred other smaller SaaS vendors that will help you get that job done.

Big data problems, or just little data problems?

There are big data problems and little data problems. Big data used to be a talking point for vendors, where sophisticated tools could handle large volumes of the same data. Think of operational data like SCADA systems. As the technology has improved, that’s no longer a bottleneck.  We’ve seen implementations in AWS, Azure, Snowflake, Databricks, {name your tool} reduce the cost of data management and processing to an immaterial amount.

The little data problems. The problems of inefficiency in collecting and reporting on data aren’t new. What is new, is the increased focus and sheer breadth of that data. There are now more tools in business that create data. As value comes at the intersection of datasets, stakeholders realize the value of combining data from system A with system B.  

Maybe it’s all about the marketing. Little problems don’t have the same ring.

Data strategy engagement at Arcurve

So, what does Arcurve do in its data strategy engagements? Typically, we look at the existing ecosystem of tools within the business and unpack the data products that exist in Excel. This includes data in reporting tools, or the applications themselves. In short, we look at your data. We get an understanding of what the landscape looks like. We attempt to understand your business’s data needs, and how information flows within the business. We reach out to business stakeholders and attempt to understand what they are trying to do.

Armed with an understanding of how data flows through your organization, we make recommendations that are both tactical and strategic at the same time. Generally, we find this works best, as you need to have some near-term tactical wins to provide some immediate business value, so you have the license to continue building a data practice. You need the license to operate to achieve strategic goals. By the same token, you want to implement the immediate tactical objectives in a way that’s congruent with your strategy. That’s why you need a data strategy.

What do we do?

• Understand the landscape (maybe we could call it datascape?)
• Identify the quick wins. Understand the hunches the business might have. What problems do you have a feeling you have but can’t substantiate? Understand the organizational structure.
• Understand the existing enterprise tools and data pipelines, and how they all fit together.
• Make short-term tactical recommendations.
• Make longer-term strategic recommendations.
• Mental models to frame data challenges, and sales tactics to help you sell your ideas internally.

We give you mental models of how you might structure your team, how you might service requests, how you might increase engagement with data in your business, how you might funnel requests made from the business, which tools you might use, and what paradigms you might follow. After all, this exercise is about outcomes.