As the Economist stated in 2017, “The world’s most valuable resource is no longer oil, but data.” Why is that the case? Today’s organization needs oil to get products to the consumer, perhaps create the product, heat the office and a whole slew of other functions that help with today’s operations. Data, on the other hand, can provide much more value over the long-term.
Armed with the right data, coupled with asking the right questions, an organization can make better business decisions across all business processes. Analytics done right can improve margins and quality, better utilize finite resources (like oil), and help employees better perform and enjoy their jobs.
Unlike oil – and what makes data so valuable – is using data to its fullest capability can create a competitive advantage. Data is not just a commodity that is required to carry out strategies and tactics. Data can help create those strategies and tactics.
Let’s dive into why more organizations are not tapping into the power of their data.
Why is data analytics and business intelligence difficult to implement?
According to IBM, 90% of today’s data was created in the last two years. With 5G networks beginning to roll out, the Internet of Things taking hold and a multitude of other factors, this trend is not going to change anytime soon. But, given the ever-expanding volume of data, organizations are going to have to tap into that power efficiently to be effective.
Where should you start? With the hype surrounding big data, unstructured data, machine learning (ML), artificial intelligence (AI), robotic worker processes (RWP) and a whole slew of other buzzwords and acronyms, it can be overwhelming to know where to start. Just a quick online search for data analytics tools yields over 500 million results. This image from FirstMark really highlights the vast array of options organizations must sift through to find the right tools for their data analytics objectives.
Though a difficult thing to implement, there are organizations out there who have determined how they can use data analytics to create a competitive advantage.
The data advantage
While your organization may not be the size and complexity of Amazon, we can use Amazon to illustrate how an organization can use data to create a competitive advantage. Amazon uses data it collects from its users to recommend products you are more likely to buy. It cobbles together data about your viewing habits, past purchases and a multitude of other data points. It then compares that picture of you to other Amazon customers. Amazon looks at the purchase/browsing history of customers similar to you to determine what it should recommend selling to you.
Instead of being inundated with choices while walking around or browsing online retail stores, Amazon is whittling away your choices and streamlining its offering to encourage you to buy from them. Check out their “recommended for you” section next time you’re shopping on Amazon for proof.
Additionally, it uses more of its data to help determine what to charge. Amazon can track when users abandon their shopping carts, remove items from a cart, apply discounts and other data points. All of this data allows them to price goods in a way that encourages you to pay the maximum amount you’re willing to spend. They find the story hiding in the data. Namely, what product do you want to buy right now and what’s the maximum you’re willing to spend? They do so by acquiring user data, asking the right questions and then answering those questions with data to make a business decision.
Data analytics in a nutshell
There is no formal definition of data analytics in Merriam-Webster’s dictionary. But, when we use the two definitions provided for data and analytics, we arrive at something along the lines of, “using factual information to examine something complex in order to understand its nature or determine its essential features.”
For example, we can use data analytics to help us answer the question, “why do sales drop 3% in the fourth quarter every year?” We would pull in sales figures and other relevant data to help us examine this complex question and determine the underlying facts and circumstances that cause this annual decline in sales.
So, how do organizations apply data analytics? Applying data analytics is a process – and like all processes, there are different levels of maturity an organization goes through.
The stages of data analytics in an organization
Initially, most organizations start out with the basics. Likely, it’s a simple Excel or Access report that provides the vital metrics for the firm. Sometimes these are done on a weekly basis or on an as-needed basis, and the report is sent out to a specific group of users.
The next stage is building out a specific analytics function. This is not as complicated as it sounds. It could simply mean the organization has someone in IT or accounting who generates reports to help answer specific and urgent business questions or problems. The big distinction between this and other stages is that this analytics function operates separately from the rest of the business. In other words, managers and executives don’t have the reporting and analysis capability at their fingertips.
Business intelligence is where the organization begins to integrate analytics across the organization. At a minimum, key executives and managers have the ability to interact with dashboards and reports to help answer their questions and see the story in the data. There’s no need to ask someone to generate a report. They can simply start filtering and visualizing data directly in the business intelligence tool. It’s also likely that as an organization matures in this stage, front-line managers also have access to these dashboards and reports to make better, faster business decisions.
Finally, there’s predictive analytics. This is where an organization is so comfortable with using data to drive business decisions, they’re no longer asking questions of the data like, “why did sales drop this quarter compared to last year?” Instead, the organization wants the analytics tool to start helping them answer questions like, “If we increase price by 2% next year, what impact does that have on the lead time needed for our various suppliers?” This stage is where you see artificial intelligence start to enter the conversation. Companies are building out predictive analytics solutions and tools that allow AI to model and forecast future results to identify the best decisions.
In this article, we’re focusing on business intelligence. We believe that most organizations have not yet achieved this maturity or at least haven’t fully realized the value of this maturity level. Once an organization is fully mature at this level, it can begin exploring the opportunities predictive analytics could provide.
The benefits of business intelligence (BI)
Let’s dig a little deeper into BI capability. Users can now analyze data on demand allowing them to more easily find the story in the data and make better business decisions. That means it’s rare for a business to make a decision and it be 100% right and remain 100% right for a prolonged period of time. Any worthwhile competitor will assess your decision and formulate their own response.
As a result, a better business decision is one that considers relevant information at the time, arrives at a decision quickly and then gathers feedback about that decision and reassesses whether an alternative decision needs to be made. If the user does change course, the user gathers more feedback about the new decision to see if another decision needs to be made.
Two critical reasons you can make better business decisions with BI:
- The user can access information and make decisions more quickly. The user no longer asks accounting or IT to pull a report. The user interacts directly with the BI tool’s report/dashboard and arrives at a decision (s)he is comfortable with faster than ever before. What if that decision was wrong? Since the decision was made quickly and the user can get feedback about the decision quickly, he or she can begin identifying alternative options to help solve the initial problem. It shortens the information-decision-feedback loop that allows an organization to be more responsive to changing market conditions. The faster you get through the loop, the faster you can respond in the market.
- Organizations can use data to complement their business instincts. How often do you procrastinate on a decision because your gut tells you to do something, but you’re unsure if you should trust that feeling? What about time spent in meetings deliberating whose opinion is right?
Edward Deming was quoted as saying, “without data, you’re just another person with an opinion.” Think about it. Without data, people are just giving their opinion about the best course of action. With limited data, at best they’re making a reasonable guess. But, by having the data at their fingertips, users can see which data points influence key metrics and begin complementing their opinions with data to help them arrive at a more informed decision. Again, the initial decision may be wrong, but they can get feedback more quickly and begin identifying alternatives and other data points that may help them arrive at a better business decision.
Getting started with BI
Start by asking what drives operational success and identify which metrics help track progress on that success factor. It can be as simple as figuring out a certain ratio, benchmark or some other standard that has shown correlation to success. If you are unsure what key metrics to track, start by asking the right business questions and using the right statistical analysis to arrive at those key metrics.
The next step is to identify the data that underlie those metrics and begin capturing it. People talk a lot about big data and how you can use ML and AI to identify the signal hiding in that data. But does that data really influence your metrics? If one of your key metrics is managing the percentage of downtime for a key machine, social media sentiment analysis is irrelevant. Determine what data drives your metrics, capture it and store it.
Finally, we need to connect that data to a BI platform that allows users to see those key metrics and make decisions tied directly to those key metrics. The organization should ensure the BI platform facilitates user collaboration (sharing of charts, comments, etc.) to allow faster conversations and quicker decisions.
Ideally, you end up with real-time access to the key metrics that drive organizational performance – a BI platform. This allows you to share visuals and reports as needed and annotate your thoughts and concerns alongside the reports. It provides the security needed to ensure the right people are seeing the right data. Most importantly, it shortens the decision-feedback loop so you can make decisions that accurately assess changing market conditions and respond quickly.
A quick way to get started is by simply asking your board, senior leadership or your boss what drives company success (or department/project success). Then start identifying key metrics or data that correlates with success and automate the compiling of that data.
Once compiled, present the data in a desired format the end user will use – even if it’s as simple as an Excel or a PDF file. As the end user begins asking for more drill-down analysis into these reports, you can show them how a tool like PowerBI could facilitate that ability even quicker. For example, you can automate the data extraction, transforming and loading, and then put that data into a complex spreadsheet that acts like a dashboard with updates and easy-to-understand visuals such as speedometer gauges (for users who feel more comfortable using Excel).
A real-world application
A business gives out bonuses to managers for their department’s performance and contribution to the bottom-line. When issuing the bonuses, senior leaders would get feedback from managers arguing that their department was entitled to more or that they believed other departments were over compensated.
This issue can be solved by building out a report that visualizes income, gross margin and other financial performance with the capability to drill down to the department level, as well as assess performance on other key output metrics.
Financial transactions can be compiled at a quarterly interval pre-2020 and then monthly thereafter to visualize the data. The data can be broken out by financial area (income, gross margin, etc.) and then by department and the identified key output metrics.
As a result, senior leaders can have better discussions with managers and have the ability to properly incentivize departments. They would even have more transparency into their financial performance throughout the year. This case is a great example of how scalable this application can be. BI can bring value to your business even at a small scale.
Tapping into the value of data is imperative to making better business decisions. Doing so could give you a competitive advantage or, at the very least, it can help you compete with organizations who are using data analytics. To tap into this value, you need efficient and effective ways of identifying key metrics, aggregating/storing data and visualizing performance on those key metrics in a way that makes it easy for your team to find the story hiding in your data.
In doing so, you will have more productive meetings, spend less time compiling data and spend more time assessing the options and executing tactics. You will make more informed decisions armed with data to supplement the stories out in the field and your gut instinct. You will have the ability to be more responsive to market changes by making decisions more quickly.
An organization’s ability to tap into its data to create competitive advantages will be crucial to its long-term success. To do so, organizations will need an efficient process in place to acquire, store and visualize data that is coming at them at an ever-increasing volume and speed.
Additionally, organizations need to know the drivers of their success and the right questions to ask to properly manage changing market conditions. By employing business intelligence, you can maximize the value of your data and find the stories hiding in your data.