Data analytics is one of the most powerful resources enterprises have at their disposal. But the value of analytics can diminish significantly if the tools and process in use are not friendly and broadly available to the business users who need them.
Afterall, these are the people who will be leveraging the data to gain insights in areas such as sales, marketing, product development, customer support, and customer experience.
“Data by itself is not analytics,” says Bryan Phillips, senior vice president of technology and CIO at Alpha Packaging, a manufacturer of bottles and jars. “At some point in time you have to understand the problems, issues, opportunities that the data is indicating, otherwise it’s just data or pretty pictures.”
Here are seven ways organizations fail to ensure that their data analytics efforts are friendly toward business users.
Forego a data strategy — or fail to align it with the business
Companies need to formalize their data strategy and align it to organizational goals, metrics, and growth, says Jeremy Stierwalt, a managing director in the enterprise data and analytics practice at consulting firm Protiviti.
“An organization operating under a clearly-defined strategy will naturally become a data broker, using its data as a key asset,” Stierwalt says. “Decisions regarding the organization’s type and amount of data, how it is collected, where it is stored, how it is accessed and used, who is responsible for it and where future data investments will be made are all important for informing the organization’s digitalization strategy and its underlying technology component.”
Analytics programs are not often aligned to the corporate strategy, and in turn are not understood nor used, Stierwalt says. “A data strategy enables the company to recognize and treat its data as a structured, comprehensive, cross-domain value-generating asset,” he says.
Exclude business users from planning and discussions
Analytics can’t be static or planned in a vacuum without input from the people who stand to gain from using the findings. The data analysts, data scientists, and others on the data management team should work directly with people in different business units to go over what’s important to them and what the data shows at particular points in time.
“Have data owners meet regularly and discuss what they see in their data,” Phillips says. “The breakthrough really happens when you can see the data across many areas.”
For example, sales users can know which customers or prospects are being called on and what orders are being generated as a result. Finance users can better understand costs and visualize revenue trends. Operations users can gain a better picture of inventory, production, and machine capacity. And marketing users can gain insight into the latest trends and which campaigns are working where.
“Each group has plenty of questions to answer,” Phillips says. “The real value is looking across [the data] for opportunities. For instance, by making data analysis available to a group of users from different disciplines, they can all know which products and services are selling and to whom and where, how many products are left in inventory and what will need to be replenished, what the profits are from sales and which items are most profitable, how marketing campaigns should be tweaked, etc.
“You would [then] look to promote those items that are hot, profitable, with capacity, and target the right customers,” Phillips says. “Or the data may tell us we need make capital investments” to boost production. “Having that cross-functional group in the room is what helps to make sure you are solving real problems or seeing real opportunities.”
Overlook your analytics audience
“When working on a business analytics project, you need to understand the audience — who they are and what data points they are expecting to see,” says Robin Allen, commercial software executive at tax software provider Vertex and a former CIO.
“As several people at different levels of an organization will be making decisions based on the data, it needs to tell a story that is understandable from their unique perspective, depending on the person’s role,” Allen says.
For example, a C-suite executive doesn’t need to see team-level metrics, but rather data that provides a more holistic view. “The story that data tells needs to be consistent, but also easy to digest by all stakeholders involved,” Allen says. “To do that, you have start with an understanding of who will be looking at the data and what insights they will expect.”
Stick to jargon instead of simplifying the message
One of the best ways to get business users disinterested in analytics is to start firing off terms they don’t understand, or that have no relevance to them. This misalignment of communication styels is the most common pitfall data analysts face when trying to deliver value from analytics, says Gautam Puranik, chief data officer and head of business strategy and analytics at automobile retailer CarMax.
That includes presentations filled with complexities and jargon, which make it difficult for users outside of the specialty area to understand. It can also apply to the way results are presented. For example, analysts commonly cite outcomes through detailed figures such as 5.238 percent rather than simplifying it to 5.2 percent or even 5 percent, Puranik says.
“Unless the decimals are really important from a decision-making standpoint, you don’t always have to showcase the complexities of the work in order to demonstrate business value,” Puranik says. “The most time should be spent not talking about what you did and how you did it, but how it will support data-driven decision-making for the business.”
This can only happen in an effective way when data analyst teams
speak in a language that’s understandable for everyone in the room, no matter the department, division, or level of expertise. “The most effective message is the one that’s most simplified,” Puranik says.
Sometimes, it’s helpful to leverage analogies. In a recent presentation, Puranik was tasked with making a case for supporting product development with an increased investment in digital marketing. “When presenting, I described the relationship to that of a car,” he says. “Unless you fill the car with gas, it won’t be of much use to you no matter how well built it is. In other words, you can’t have one without the other. This resonated with everyone in the room and I received the necessary approvals to move forward.”
Underestimate the power of a picture
When trying to gain quick insights, many people prefer to see pictures or graphic depictions of concepts. So oftentimes the way to drive home data findings is to visualize them.
“A picture tells the story,” Phillips says. “This could be graphs, Venn diagrams, or other visualizations. I am still a fan of drawing the data out in [Microsoft] Excel or even on a board for starters.” Then move on to more advanced visualization products to provide more depth or sophistication to the findings — while keeping it understandable.
“Making the visualization friendly comes with seeing real large business problems displayed in a concise, easy-to-understand way,” Phillips says. “This takes skill, which is why you should start with people who are good at this first and let others learn from them.”
After business users see the output of the data analysis in some visual form via data visualization tools they will likely be more interested, Phillips says. Many will want to be trained themselves or have their teams trained so they can create their own visualizations without the need to lean on the data analytics or IT staff.
The use of data visualization, particularly when it involves high-level executives, can actually help fuel future growth of these tools. “When a C-suite member comes with charts, graphs, and diagrams telling a clear story, the company starts making more profitable decisions and it hits the bottom line,” Phillips says. “Now investment in analytical tools, training, people becomes much easier.”
Go for comprehensive rather than comprehension
Just as you need to simplify your language, you also need to simplify your presentation materials such as slides, Puranik says. “The title of any slide should succinctly summarize the page’s content,” he says. “While the saying ‘pictures speak louder than words’ still rings true, charts and graphs should be prefaced by a clear summary of [their] findings.”
Graphs and charts should also be kept clean and digestible, with no more than two graphs per slide and 10 slides total. “Remember, you’re not writing a suspense novel; don’t make your audience guess what the most important points are,” Puranik says.
Fail to think like a business person
Data analytics professionals in some cases might be a bit removed from the day-to-day tasks that are important to business users. They might need to reframe their thinking so as to better relate to what users need in terms of data and analysis.
“Consider yourself a business leader, not just an analyst,” Puranik says. “Think as if you’re owning the business and you’re using data to make decisions. What would be your learnings coming out of the work that you’ve done?”
Identify research-backed recommendations and communicate them clearly, Puranik says. “Data analytics is a tool, not an outcome; how can you make your findings outcome-driven?” he says. “Organizations that are most successful at delivering business value using data analytics are the ones that believe data analytics to be a core competency, not just an important competency.”
When preparing presentations to groups of business users, seek feedback from managers and mentors first to help fine-tune the presentation for a business audience.
“At the start of my career when I was fresh out of graduate school, I was given the opportunity to use predictive models and analytics to drive business impact,” Puranik says. “As you can imagine, I was very excited at the prospect of being able to drive real change for a company.” He spent days creating a presentation full of complex graphs and detailed notes.
“After presenting to business partners, I was elated, I was proud of my work and confident that I did well,” Puranik says. “A few short days later my boss shared the feedback that while it was in fact great work, I needed to strengthen my communication skills. Only about 2 percent of the people in the room understood what he was talking about, the boss said, and the other 80 percent were completely lost.
“After a few days of reflection, I realized that she was right,” Puranik says. “The presentation ended up being a diary of the work I did. I learned the valuable lesson that, when making a business case, you shouldn’t present on the value of your work but the outcomes that your work will drive.”