Incorporating data analytics into operations could save time and money | Baker Tilly
Article
How and why to incorporate data analytics into your organizational thinking
Sept. 27, 2020
The advancement of modern computers and the expansion in data visualization tools have created a big shift in focus toward data analytics. Data analytics relies on information organizations already have to make better decisions about issues they’re facing on a consistent basis.
There are plenty of narratives out there about artificial intelligence (AI) and machine learning that relate to data analytics, but within your organization most of the data work being done is probably much less complex.
Often what falls under the data label at a firm is as much about reporting as it is analysis. Data science or data analytics can sound complex, but most likely refer to processes you already have in place.
Here, we outline the benefit of being a data-driven organization, pain points, potential solutions, and next steps to help your organization integrate data analytics into its operations.
Benefit
Integration
Adding empirical information, brings an element of rigor that can elevate an experience-driven assessment to a building block of a data-driven environment.
Analytics exist in a broad spectrum that includes everything from the highly qualitative assessment to the entirely quantitative statistical model. Crossing the chasm between intuition and data is frequently a back-and-forth match that requires multiple iterations to get the right measures, context, and delivery.
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Time savings come from centralizing data, automating a dashboard, or doing something similar to provide a consolidated source of facts. Once they have easy access to actionable data, many team members start arriving at deeper insights almost immediately.
It often takes several metrics — for example time, customers, locations, or number of employees — together in one place to give the appropriate level of context and make sense of important operational data. It’s common to see a developed sales forecast based on a few client interviews or subjective experience that still needs more details or review. It’s also common for piles of collected data to remain in incomprehensible spreadsheets disconnected from operational strategy.
Context brings meaning to data, and often you have to make an assessment up-front to determine which data will bring greater credibility or clarity to the desired outcome.
The bottom line: for data analytics to be most useful, data must be collected intentionally, analyzed by a person who understands its greater meaning, and delivered in a form and format that facilitates understanding its significance.
Time-savings
When your organization is trying to find the benefits for using more data, there are many different considerations. Companies often cite time-savings as one of the main reasons for shifting towards a data-focused culture.
Time savings come from centralizing data, automating a dashboard, or doing something similar to provide a consolidated source of facts. Once they have easy access to actionable data, many team members start arriving at deeper insights almost immediately.
With the rising trend of working remotely, getting everyone together to come up with a report or an answer to a question is both more difficult and more time-consuming. Companies that have solid record-keeping systems are gaining some benefit, but the biggest wins occur around gaining immediate access to the information you need.
Pain points
Here are some common challenges of data analytics.
Measurements
Data is a record of what was measured. It will always be up to the people involved to make sure that those measurements are timely, accurate, and appropriate reflections of the information collected.
Accessibility
Making data accessible to the right people can be difficult. Many organizations have excellent, robust data systems such as an enterprise resource planning (ERP) system, General Ledger (GL) system, or fleet management system. However, getting exactly what you need out of them can be a slow and painful process.
For example, consider the total time every team member spends trying to get answers from an ERP system. That time could total hundreds or thousands of hours per year. One time-saving solution could be centralizing pre-curated data or creating automatic reports.
Consistency
It’s about removing the guesswork and human error as much as possible so facts can shine through. People are hardwired genetically to make decisions with emotions which can cause unpredictable results in most situations. Data and data analytics are both about shining a light on organizational blind spots so a broader, more accurate, perspective is achieved.
The ideal situation is to have consistent data processes, so data facts and measurements form a reliable basis for decision-making. The need for specificity and consistency is why the term data science is often used to refer to data analytics.
Analysis
Data analytics is still an art. It’s possible to accurately measure elements you think are initially relevant to a desired outcome. Later on, you could discover the identified data elements don’t end up influencing the actual process you’re looking to analyze.
It’s also possible to do an improper analysis of the data used to measure the process. Not all measurements provide a perfect read of a given scenario. In either case, the data isn’t really the problem — it’s the use of it. While this might make you feel apprehensive about using data, some solutions are outlined below for you to explore.
Potential solutions
Testing
While there aren’t perfect measures of complex business processes, there may be good proxies — accurate measurements of part of the item you’re investigating. Typically, if you’re looking at trends, a proxy can be helpful even if it doesn’t offer an entire solution. With data, it can be helpful to experiment and try different measures, then test those measures against various scenarios.
Knowledgeable staff
Additionally, there are almost certainly people who have been in a similar situation before you and many are willing to share. Having staff who are fluent in data, statistics, or both can be a huge help.
There’s no replacement for depth of experience and having staff deeply integrated in business operations to develop a holistic approach and thoughtfully integrate the relevant data. If these people don’t currently exist within your organization, hiring or contracting an experienced data analyst or team can be an excellent option.
Whether you need something technical or just a general review of your work, hiring an experienced professional can save significant time and energy. Often renting the team you need for a short-term project is less expensive than trying to create one, and it could be a valuable stepping stone in terms of the insight provided to your organization.
Centralized data
Consider the difference between creating a report in a spreadsheet versus having a fully automated dashboard. Both provide answers, but in many cases, spreadsheets take longer and are more likely to exist only on one folder or machine. Access can be difficult and these reports are more prone to human error. A curated dashboard, on the other hand, can be configured so that it’s accessible by whomever might need to see it, virtually anywhere, anytime.
An automated dashboard can also save the trouble of assembling each of the data elements every time with a nice bonus of multiple team members being able to use that data source for other things as they arise. Typically, centralized data sources are a great time-saver, more so than automated reports because questions about the data inevitably change over time.
Having access to all of the data on one screen can often allow the users to get to an answer by visually analyzing the issue. Context, rigor, and the numbers all come together to bring insight and provide more complete analysis for industries.
The data-driven environment is broad. There are myriad ways to add more rigor to a process or report, and there are probably as many opinions on how to go about this as there are types of data analytics. The important thing to remember is data analytics is a journey and not a destination. Regardless of where you or your organization is starting, there’s always a next step.
Next steps
The process of becoming more data driven can seem daunting.
Below are a few ways to get started.
Decide to become data-driven
The impact of data analytics on an organization depends on many variables, but the willingness to incorporate data in decision processes is by far the most important determinant of success. Team buy-in is crucial. Once you’ve made your commitment, getting people from around the organization on board will often require effort, time, and persistence.
Assess your current status
The second step is to assess your organization’s current procedures:
How do you use data now?
How long does it take team members to get to the data?
Do you feel like you are getting the most out of your data?
What are your industry peers and competitors doing with data?
What questions do you ask, and which ones get asked repeatedly?
This initial assessment needs to be impartial and accurate.
Create a plan
Ideally your organization’s plan to incorporate data analytics into its operations would have three parts:
End-state, or high-level, goals
Two-year, or mid-range, goals
Short-range goals, limited to your current quarter or current half of the year
Your needs, direction, and approach to data will grow and change. Focusing on what’s practical for a two-year horizon is a good compromise between your overall end-state goals and day-to-day necessities.
Continuously update your plan
The plan will need to be adjusted, so keep notes and track what you and your organization have accomplished. As your steer your team towards the goals you’ve set, take a moment to check your status and adjust your plan.
The business environment and your goals will inevitably change; it’s important to have a pragmatic, but determined, approach to keeping your plans up-to-date.
