Everyone wants to claim data-driven success because we know that data is important for building and executing successful strategies. But with this goal comes one essential question — how do we successfully incorporate data into planning and execution?
Will Ritchings, Director of Revenue Strategy and Operations at SecurityScorecard, dissects the challenges of data management, shares his insights on effective dashboard design, and unravels the key elements for building a successful data strategy.
Data management challenges and solutions
A common downfall seen in many data-driven roles and environments is companies often don’t have as firm a grasp on their data management as they might think. They either have too much data or too little data. Think of it in terms of a data ocean versus a data desert — rarely, is there an effective balance.
With data being so relevant everywhere, it is important to really define its role at certain junctures of a company.
“As a company is going through the process of looking at their annual plan or goals they want to accomplish, data can help demystify some of the variables or assumptions,” Ritchings says. “In addition, while you’re halfway through those goals and are tracking progress, you’re going to rely on your KPIs and metrics as well. It really is ever present, but what data points you track is paramount to success.”
The next most common challenge in data management comes when a company is tracking all the most important and relevant data points, but it comes to the point of executing their current plan and they have no idea how to actually access that data where it resides.
In these cases, the organization likely has a data lake, or too much data, rather than not enough. They know they have records of all the data that ever existed, but the idea of finding the exact point they need which will feed into a system they can rely upon is too elusive and, often, too overwhelming.
These cases require a management process that will help the organization get a hold of their data and put it into a theoretical headlock where stakeholders can then get a better idea of where the data actually resides and how they can use it to their advantage.
Determining the location of necessary data becomes easier if someone asks the right questions about the process for gathering data, how it is used, and what the company’s outstanding goals are.
“When I’m working with my customers and internal stakeholders, I ask them to describe things in Salesforce speak,” Ritchings says. “I ask them to tell me the filters and criteria they are really getting at. A lot of times, that’s a challenge, but the goal of an analytical or data-focused team is to translate an ask or request into the data language that you’re going to get the results out of.”
Automating and governing data for better reliability
Data’s reliability is challenged by its elusive nature. Overcoming these challenges involves translating those stakeholder requests, implementing robust data governance, and ensuring data accuracy and freshness through better automation.
It can be difficult, especially in high-growth companies, to lock down that single source of truth and answer the question ‘Who is the key holder for that data or report?’ and then support it with the proper governance and data freshness policies.
The right governance and data freshness policies help keep data from getting out of sync or becoming so stale that the data might drive strategy in the wrong direction.
But what does data governance and freshness actually mean?
“Ultimately, it’s about the quality, accessibility, and frequency of the data. It’s knowing who needs it, where it resides, and where it has to get to — whether your data is structured or unstructured, and when it arrives at its destination, is it arriving in the format that’s usable and going to maximize the value for your end users?” Ritchings says.
Spending too much time on maintenance then gets in the way of growth and progress in the long run, so it’s better to keep automation, governance, and freshness top of mind when planning and as plans are executed.
Effective dashboard creation for data-driven decision-making
The point of having data is to use it to make informed, insights-driven decisions in both the strategic planning process and in course correction along the way. Most people think of data in terms of numbers, percentages, and dollars, but at some point, additional context must come into play.
Think of an art museum. It is certainly enjoyable to walk around an exhibit and just appreciate what you see, but you gain more insight and knowledge by reading the placards that accompany a piece. With context, visitors are either able to appreciate the art more or less.
Data is the same way. With only a number on some sort of marquee, the human element of context is missing. This is where effective dashboard creation really matters, and the focus should be on presenting the right information.
“The tendency is to get that periodic table-style dashboard with every single thing imaginable, but when you go to operationalize it to support a strategic discussion, you’ll find that people have competing priorities and conflicting interests. They tend to want to focus on the things they know the most about and the biggest red flags,” Ritchings says.
The periodic table-style dashboard often needs to be shrunken down or split up to allow for effective discussions around a singular topic. In many cases, this is where a data leader or chief of staff may come in and help guide the conversation.
In best-case scenarios, this data guru will be unbiased.
“It should be someone technical like a manager or someone within DevOps who’s working day in and out with the metrics, data points, and stakeholders for specific departments, but is also not incentivized by the performance of a given metric,” says Ritchings.
Because this leader is unbiased they can focus less on successes and failures and more on the order of the metrics. They are better able to determine if a data point is even something worth looking at.
If data is impactful, they can encourage the team to talk about it. Likewise, if it is not impactful, they can tell the team to take it off the dashboard.
Balancing leading and lagging data for an ideal dashboard
Once the dashboard is up and going with all the data it’s going to display, leadership will likely be very excited to see it and pay attention to it frequently. However, this opens the door for wrong impressions.
Leaders may see a small change and redirect resources to impact that change when, instead, it would have been optimal to wait for an update that addresses said change. This is where leading and lagging metrics play a major role — knowing that lagging metrics will take a while to be impacted.
But is there a formula for the perfect or ideal balance between leading and lagging metrics on a dashboard? The answer to this question is likely no, there is no formula.
“You have to have a healthy dosage of both leading and lagging metrics. But, it’s more about how you use data throughout a process, if it’s quarterly, or the fiscal year, and at what points you’re looking,” says Ritchings.
Building a successful data strategy
Say you’re on the other end of this spectrum. You have some data, but it’s more like a data desert than a data lake. There just isn’t quite enough to build the story you need to tell. How can you get started building your data strategy and architecture?
Perhaps the best initial advice is to start building with the end goal in mind. This begins by defining your ‘need-to-haves’ and ‘nice-to-haves’ and prioritizing accordingly.
“Put data freshness policies in place and set up a safety net so that if a job does fail, you’ll get an email or notification with who to contact,” Ritchings says. “Automate as much as possible — not so much that you lose focus of it or it becomes completely out of sight out of mind, but enough you still maintain that sense of governance.”
The driving force behind building a successful strategy is deliberate decision-making with the end goal in mind. This process could take months, quarters, or even years, but with solid strategy and transparent communication, the crawl, walk, run process should lead to clear ownership and simple maintenance.
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