Your business has data — but are you using it?
Conceptualizing what it means to be a “data-driven” business is a lot easier if we start with first principles.
Every company has numerous sources of data that need to be brought together for the business to run. Product catalogs, customer lists, marketing activities, and sales transactions — we can go on and on. But often that data sits within different business verticals, on different servers, and in different files. If you’re going to use all of that data to run your business — if you’re going to get any value from it — you need to first be able to use it.
At Enharmonic, we think that the principles of using data are actually straightforward. When we break it down, there are really 3 core things we need to do:
- Model the data
- Bring the data together
- Use data to ask and answer “Why”
Let’s walk through this.
A simple model of a company
When you boil it down, a company is really just a machine. The machine has multiple parts, and different people interact with these parts in numerous ways. A supplier sends raw materials in exchange for money. A customer exchanges money for a finished product. Most people outside of the company only ever interact with it in narrowly-defined ways, making the internal workings something of a “black box.”
In many companies, this fragmented view of actions is unfortunately the internal reality as well. Communication and activities are often segmented by function — Supply Chain rarely talks to Sales, Sales rarely talks to Marketing— so it can be difficult to develop a unified view of the business as a whole.
That’s where data comes in.
What is data, really?
Let’s ignore buzzwords. We can think of data as a “digital footprint” of the actual real-world activities that a company performs. A sale took place? There’s a database entry for that. A marketing campaign just finished? There are email lists and records of open rates.
So how does this “footprint” help with our whole business view? We just need to follow those 3 steps mentioned above.
Step 1. Model the Data
From the perspective of a business manager, what actually matters is not the bits of data themselves, but the real-world concepts and activities that they represent. To quickly make decisions, we’d prefer to look at data in a way that mirrors the real-world activities it represents. This means thinking in terms of “Customers” and “Products,” not disparate rows of data from tables and Excel sheets.
To do this, we think critically about what real-world “things” our data represents. A single piece of source data might actually represent multiple distinct business concepts. For example, a customer, an address, and a phone number are commonly found in a single row of an Excel file, but actually correspond to different ideas in the real-world that don’t necessarily have a one-to-one relationship. This insight becomes very important for correctly conceptualizing our data.
Once we’ve disentangled our data into distinct business elements and relationships, we can move on to step 2.
Step 2. Bring the data together
While most users shouldn’t care where their data is actually stored, they should definitely care about the interaction that a particular data system makes possible. For the types of questions we’re going to have, it will be easier to explore and analyze our data if it’s all in one logical place. We also want to make sure that all the distinctions we’ve defined during our data modeling step are stored explicitly — that way we don’t have to recreate the wheel every time we want to access our data.
Now since we’ve already modeled our data, the difficult part is actually complete. Modeling requires thinking, but moving data from one place to another is grunt work. In a more perfect world we might even offload this part to a computer, with execution, monitoring, and validation handled automatically. Human time is best spent being creative as opposed to managing the repetitive processes of moving data around, or worrying about query language syntax.
Step 3. Use data to ask & answer “Why”
Asking who, what, where, and when is often an elementary activity. (Who purchased what product, on which day, at which store, and for what price?). The real goal of the business manager should be to answer the why — that’s what leads to the deeper understanding of cause and effect that can drive decision making.
Continuing with our machine metaphor, we can think of the controls that a business manager has to drive the business as “levers.” Pulling different levers can alter the way in which the business operates — speeding things up, changing direction, or shifting internal pieces around. Understanding a business means having a feel for what levers to pull.
Using our data is just a methodical process for building an understanding of what outputs the business is capable of producing, and how to optimize the inputs in order to generate the outputs we’re looking for.
How do we go about doing this? In the same way we go about understanding anything — by starting at the most vital areas and working outwards.
We get to why by asking and answering questions, forming hypotheses, and testing those hypotheses. As we get closer to developing the understanding of why, we can actually start to be predictive about the markets we’re operating in, and make informed decisions about what levers to pull to drive value for the business.
Great. How can we actually do this?
The steps laid out above may make conceptual sense, but can still feel difficult to pull off. An organization may feel like they’re missing the time, talent, and systems needed to execute this plan.
That’s why Enharmonic was created — to make these steps incredibly straightforward for a business manager to do. Without turning this into a pure marketing pitch, we think that a code-free, visual approach to working with data — driven by a ton of automation — can really change the game for a lot of companies out there.
Whether or not you want to check out our software, hopefully this principles-based walkthrough to actually using data to drive your decisions has given you some food for thought! We’d love your comments, as well as what data-related issues you face on a daily basis. Feel free to drop us a line at info@enharmonic.ai
If this problem space is interesting to you— we’re hiring! Let us know why you find this concept interesting, and we’ll be in touch…
