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Data-Driven Decisions Improve Decision-Making Processes

Data-Driven Decisions: Streamlining Decision-Making Processes



You have a hunch, a gut feeling, that your company's expenses are going up due to increases in material costs. You can see the rising trends month to month in your P&L spreadsheet and get to the individual accounts causing the increase.

You try to get to "Why?" and realize you cannot easily – the data are not readily accessible – so you push off digging in until the next month shows another increase.

When you do finally dig into the data, it's time-consuming but worth it: a specific supplier has doubled their cost for a set of critical materials. You check with the procurement team and identify another supplier that offers the same material at a lower cost.

Understanding Data-Driven Decisions

Leveraging data can be difficult, but getting to a place where data drives decisions within your company can empower informed decisions. You move away from inclinations or assumptions and toward facts.

What is Data-Driven Decision-Making?

Data-driven decisions leverage timely and accurate information in the decision-making process. When faced with opportunities, having pertinent information at the right time consistently clarifies the next steps.

We as humans naturally practice this behavior each day – we research information online before buying something, ask a friend about their experience at a local restaurant before booking, and check our smartwatch for the weather prior to dressing.

Why, then, is it so difficult within our workplaces to leverage information to make decisions?

Common Roadblocks to Data-Driven Decision-Making

The easy answers include inaccessible, unclean, inaccurate, and untimely data.

Frequently, we encounter customer scenarios where the business logic on how metrics are to be calculated or even their base definitions are not agreed upon throughout the organization. Misalignment in timing, expectations, ownership, and quality also all contribute to the difficulties in leveraging data in decision-making. Lastly, and probably the actual root cause of the above issues: data frequently are not upheld as a company asset.

We take for granted that our systems capture and store data. Our data become like the junk drawer we all have at home: a collection of random, non-associated items that without organization and care can quickly overwhelm and become a mess (think: broken rubber bands, vitamins, a half-set of birthday candles with icing on them, pliers, and an expired coupon book from the early 2000s).

Is Your Data Handled as an Asset?

Think about your growth decisions in strategic areas of your business:

  • Is the decision to invest in one business unit versus another easier when you can see clear performance trends for each?
  • Should we sell off one entity as it continually underperforms compared to the others?
  • Do we need more employees in a sales region to maximize potential sales volume, and how can we better balance employee workload region to avoid unnecessary overtime?

Answering these questions begins with an organization that is ready to treat data as an asset.

There is an inherent investment (time and money, as with any asset) to get to a place where your data works for you, but disparate data sources or unclean data will not get you much closer to data-driven decision-making. Starting where you are is a great move in establishing a culture within your organization that treats data as an asset and fosters a data-driven mindset amongst employees.

Reducing the overall manual reporting effort is also a sure way to ease month-end or board reporting preparation nightmares. Providing access to users at an appropriate level brings informed decision-making to all levels of the organization, inspiring trust and improving day-to-day workloads for people.

Great! So how do we get there?

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Data Strategy, Data Centralization, and Data Visualization

Your process can start where you are now. By defining the end goals up front and identifying the data required to get there, you create a targeted and achievable effort that can expand as necessary.

At SVA, we like to partner with organizations and stay people-focused. Strategically, we help identify what information, if automated and surfaced in a consumable format, would significantly enhance and lighten a person’s day-to-day workload. These items typically roll up into organizational impact.

From a technical perspective:

  • Data centralization across your organization's data sources (Acumatica ERP, Salesforce, Excel, IOT, etc.) allows you to establish sources of truth and model your data for accurate, timely, and repeatable reporting. This process helps automate your data tasks and provides structuring and cleaning opportunities to get data ready for reporting. It is achievable with a database (such as Snowflake), a data orchestration and transformation tool (such as Matillion), and a strategic internal or external data partner or team.
  • Data visualization plays a crucial role here, surfacing actionable insights or exploratory data to help guide your decisions (in tools like Tableau or Power BI).

Leveraging Data for Decision-Making

The questions posed above become much easier if we have relevant data available.

Let's take our employee overtime scenario above. By word of mouth, we hear a certain division in our company has two employees working far more overtime than the rest of the team. We want to know: 1) is this true, 2) what's the associated cost, and 3) what can we do about it?

Let’s assume we have our data centralized, cleaned, structured, and ready for reporting. Here's a visual showing employee monthly overtime for several operations employees in a division.

Data Driven Decisions Image (1) (1)

We see clearly that two employees do indeed capture significant overtime each month, while other team members are underutilized. The associated cost is high as they begin hitting 1.5x or 2x pay. Having the facts in hand we can now have informed data-driven conversations and make clear data-based decisions.

While this is a simple scenario, it's probably relatable. Little visibility into true activities, processes, and finances within your organization is a quick way to reactionary decision-making. These decisions are aimless and typically only solve symptoms without identifying the root cause.

Benefits of Data-Driven Decision-Making

There are numerous benefits to leveraging data for decision-making, but we will list a few of our favorites here:

  • Facts do the talking. No more word of mouth or hunches. Transparency can hurt when it uncovers root causes, but the growth is worth it.
  • You get reliable and accurate results that are repeatable.
  • It’s fast. You get same-day answers instead of waiting on a weeks-long manual analysis.
  • This may be a side effect of the data centralization build process, but people elevate to their true role by having manual reporting tasks handled via automated data solutions. Think: a happier, more productive workforce.

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How to Begin Your Data-Driven Decision-Making Journey

We have stated it previously, but starting where you are is a great place to begin.

  • Establish the end goal and identify the steps necessary to treat data as an asset within your organization.
  • Build data architecture as necessary and surface actionable information to your employees.
  • Visualize your data and permit proactive decision-making based on facts and trends.

You will decrease the time-to-insight and improve people’s day-to-day, ultimately uncovering what makes your business thrive.

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Authored by: Jacob McClendon

Authored by Jacob McClendon

Jacob is the Director of Analytics with SVA Consulting. In his role, Jacob assists clients by strategically architecting solutions that help unlock meaning in data. Alongside a strong team, he designs and builds client-specific data automation and visualization solutions to fit the unique needs of a given client.

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