We read about it everywhere. The term ‘big data’ alone has become something of a buzzword in recent times – and for good reason.
By leveraging the wealth of digital insights available at your fingertips and embracing the power of business intelligence, it’s possible to make more informed decisions that will lead to commercial growth, evolution, and an increased bottom line.
By implementing the right reporting tools and understanding how to analyze as well as to measure your data accurately, you will be able to make the kind of data driven decisions that will drive your business forward. Of course, this sounds incredible in theory.
But in practice, even if you have access to the world’s greatest data, it’s possible to make decisions that disregard tangible insight, going with your gut instead. In most cases, this can prove detrimental to the business.
While sometimes it’s okay to follow your instincts, the vast majority of your business-based decisions should be backed by metrics, facts, or figures related to your aims, goals, or initiatives that can ensure a stable backbone to your management reports and business operations.
To help you on your quest towards analytical enlightenment, we’re going to explore data driven decision making, study the importance of data driven decision making, and examine some real-world examples of turning insight into business-boosting action.
What Is Data Driven Decision Making?
Data driven decision making (DDDM) is a process that involves collecting data based on measurable goals or KPIs, analyzing patterns and facts from these insights, and utilizing them to develop strategies and activities that benefit the business in a number of areas.
Fundamentally, data driven decision making means working towards key business goals by leveraging verified, analyzed data rather than merely shooting in the dark.
However, to extract genuine value from your data, it must be accurate as well as relevant to your aims. Collecting, extracting, formatting, and analyzing insights for enhanced data driven decision making in business was once an all-encompassing task, which naturally delayed the entire data decision making process.
But today, the development and democratization of business intelligence software empower users without deep-rooted technical expertise to analyze as well as extract insights from their data. As a direct result, less IT support is required to produce reports, trends, visualizations, and insights that facilitate the data decision making process.
From these developments, data science was born (or at least, it evolved in a huge way) – a discipline where hacking skills and statistics meet niche expertise. This fairly new profession involves sifting large amounts of raw data to make intelligent data driven business decisions.
The ‘gold’ that data scientists ‘mine’ comes in two distinctive types: qualitative and quantitative, and both are critical to making a data driven decision.
Qualitative analysis focuses on data that isn’t defined by numbers or metrics such as interviews, videos, and anecdotes. Qualitative data analysis is based on observation rather than measurement. Here, it’s crucial to code the data to ensure that items are grouped together methodically as well as intelligently.
Quantitative data analysis focuses on numbers and statistics. The median, standard deviation, and other descriptive stats play a pivotal role here. This type of analysis is measured rather than observed. Both qualitative and quantitative data should be analyzed to make smarter data driven business decisions.
Now that we’ve explored the meaning of decision making in business, it’s time to consider the reason why data driven decision making (DDDM) is important.
"Information is the oil of the 21st Century, and analytics is the combustion engine." – Peter Sondergaard
Why Data Driven Decision Making Is Important?
The importance of data in decision lies in consistency and continual growth. It enables companies to create new business opportunities, generate more revenue, predict future trends, optimize current operational efforts, and produce actionable insights. That way, you stand to grow and evolve your empire over time, making your organization more adaptable as a result. The digital world is in a constant state of flux, and to move with the ever-changing landscape around you, you must leverage data to make more informed and powerful data driven business decisions.
Data driven business decisions make or break companies. This is a testament to the importance of online data visualization in decision making.
MIT Sloan School of Management professors Andrew McAfee and Erik Brynjolfsson once explained in a Wall Street Journal article that they performed a study in conjunction with the MIT Center for Digital Business. In this study, they discovered that among the companies surveyed, the ones that were primarily data driven benefited from 4% higher productivity as well as 6% higher profits.
Companies that approach decision making collaboratively tend to treat information as a real asset more than companies with other, more ambiguous approaches.
10 Tips And Takeaways For An Enhanced Data Driven Decision Making Strategy
Finally, here are 10 practical tips and takeaways for better data driven decision making in business. By the end, you’ll be 110% sold on the importance of making these kinds of decisions.
1) Guard against your biases
Much of the mental work we do is unconscious, which makes it difficult to verify the logic we use when we make a decision. We can even be guilty of seeing the data we wish was there instead of what’s really in front of us. This is one of the ways a good team can help. Running your decisions by a competent party who doesn’t share (or even know) your biases is an invaluable step.
Working with a team who knows the data you are working with opens the door to helpful and insightful feedback. Democratizing data empowers all people, regardless of their technical skills, to access it and help make informed decisions. Often this is done through innovative dashboard software, visualizing once complicated tables and graphs in such ways that more people can initiate good data driven business decisions.
With more people understanding the data at play, you’ll have an opportunity to receive more credible feedback. The proof is in the numbers. A 2010 McKinsey study (which is helpful to read even today) of more than 1,000 major business investments showed that when organizations worked at reducing the effect of bias in their decision making processes, they achieved returns up to 7% higher. When it comes to data driven decision making (DDDM), reducing bias and letting numbers speak for themselves make all the difference.
Tips for overcoming a biased behavior
- Simple Awareness – Everyone is biased, but being aware that bias exists can affect your decision making can help limit their impact.
- Collaboration – Your colleagues can help keep you in check since it is easier to see biases in others than in yourself. Bounce decisions off other people and be aware of biased behavior in the boardroom.
- Seeking out Conflicting Information – Ask the right questions to yourself and others to recognize your biases and remove them from your decision process.
By eliminating bias, you open yourself up to discovering more opportunities. Getting rid of preconceived notions and really studying the data can alert you to insights that can truly change your bottom line. Remember, business intelligence shouldn’t only be about avoiding losses, but winning gains.
2) Define objectives
To get the most out of your data teams, companies should define their objectives before beginning their analysis. Set a strategy to avoid following the hype instead of the needs of your business and define clear Key Performance Indicators (KPIs). Although there are various KPI examples you could choose from, don't overdo it and concentrate on the most important ones within your industry.
3) Gather data now
Gathering the right data is as crucial as asking the right questions. For smaller businesses or start-ups, data collection should begin on day one. Jack Dorsey, co-creator and founder of Twitter, shared this learning with Stanford. “For the first two years of Twitter’s life, we were flying blind… we’re basing everything on intuition instead of having a good balance between intuition and data… so the first thing I wrote for Square is an admin dashboard. We have a very strong discipline to log everything and measure everything”. That being said, and done, implementing a business dashboard culture in your company is a key component to manage properly the tidal waves of data you will collect.
4) Find the unresolved questions
Once your strategy and goals are set, you will then need to find the questions in need of an answer, so that you reach these goals. Asking the right data analysis questions helps teams focus on the right data, saving time and money. In the examples earlier in this article, both Walmart and Google had very specific questions, which greatly improved the results. That way, you can focus on the data you really need, and from bluntly collecting everything “just in case” you can move to “collecting this to answer that”.
5) Find the data needed to solve these questions
Among the data you have gathered, try to focus on your ideal data, that will help you answer the unresolved questions defined at the previous stage. Once it is identified, check if you already have this data collected internally, or if you need to set up a way to collect it or acquire it externally.
6) Analyze and understand
That may seem obvious, but we have to mention it: after setting the frame of all the questions to answer and the data collection, you then need to read through it to extract meaningful insights and analytical reports that will lead you to make data driven business decisions. In fact, user feedback is a useful tool for carrying out more in-depth analyses into the customer experience and extracting actionable insights. To do this successfully, it’s important to have context. For example, if you want to improve conversions in the purchasing funnel, understanding why visitors are dropping off is going to be a critical insight. By analyzing the responses in the open comments of your feedback form (within this funnel), you will be able to see why they’re not successful in the checkout and optimize your website accordingly.
7) Don’t be afraid to revisit and reevaluate
Our brains leap to conclusions and are reluctant to consider alternatives; we are particularly bad at revisiting our first assessments. A friend who is a graphic designer once told me that he would often find himself stuck towards the end of a project. He was committed to the direction he had chosen and did not want to scrap it. He was invested, for the wrong reasons. Without fail, when this happened he would have to start all over again to see the misstep that got him stuck. Invariably, the end product was light-years better reworked than if he had cobbled together a solution from the first draft.
Verifying data and ensuring you are tracking the right metrics can help you step out of your decision patterns. Relying on team members to have a perspective and to share it can help you see the biases. But do not be afraid to step back and to rethink your decisions. It might feel like a defeat for a moment, but to succeed, it’s a necessary step. Understanding where we might have gone wrong and addressing it right away will produce more positive results than if we are to wait and see what happens. The cost of waiting to see what happens is well documented…
8) Present the data in a meaningful way
Digging and gleaning insights is nice, but managing to tell your discoveries and convey your message is better. You have to make sure that your acumen doesn’t remain untapped and dusty, and that it will be used for future decision making. With the help of a great data visualization software, you don’t need to be an IT crack to build and customize a powerful online dashboard that will tell your data story and assist you, your team, and your management to make the right data driven business decisions. For example, you need to have your finances under control at all costs:
An outline presented on a financial dashboard will ensure an at-a-glance overview of the financial performance of a company. With the top KPIs such as operating expenses ratio, net profit margin, income statement, and earnings before interests and taxes, this dashboard enables a fast decision making process while concentrating on real-time data.
For further inspiration, look at these incredible data visualization examples from some of the world’s most forward-thinking brands and businesses.
9) Set measurable goals for decision making
After you have your question, your data, your insights, then comes the hard part: decision making. You need to apply the findings you got to the business decisions, but also ensure that your decisions are aligned with the company’s mission and vision, even if the data are contradictory. Set measurable goals to be sure that you are on the right track… and turn data into action!
10) Continue to evolve your data driven business decisions
This is often overlooked, but it’s incredibly important nonetheless: you should never stop examining, analyzing, and questioning your data driven decisions. In our hyper-connected digital age, we have more access to data than ever before. To extract real value from this wealth of insights, it’s vital to continually refresh and evolve your business goals based on the landscape moving around you.
Data Driven Decision Making Mistakes You Should Avoid At All Costs
At this point, the importance of data in decision making is clear. But while understanding the dynamics of data driven business decisions and exploring real-world data driven decision making examples will steer you in the right direction, understanding what to avoid will help you cement your success.
How many times in your life have you prepared for a meeting, had the facts and figures ready to go, and in the end the decision goes the complete opposite direction?
It probably felt like the decision had been made before the meeting even began. If this sounds familiar, you are not alone. We aren’t just talking about a startup full of newbies who think going with their gut is more critical than KPIs; we are talking about huge companies. Rob Enderle, a former IBM employee and Research Fellow for Forrester wrote a fabulous article which documents the shortcomings of executives at IBM and Microsoft.
While the article is packed full of examples, perhaps the most egregious is IBM’s partial sale of its ROLM division to Siemens. Enderle and team produced an internal report that proved selling to Siemens would be a catastrophic failure. It turned out that the decision had been made before the research came out. In fact, executives forgot the research had been commissioned at all. Their gut decision ended up costing the company over one billion dollars.
A publication from BI-Survey shows us that 58% of the companies they surveyed said that they base at least half of their regular business decisions on gut feel or experience, instead of being data and information-driven. On average, they realized that the companies would use only 50% of the information available when it came to decision making.
As business intelligence providers ourselves, we understand the importance of data driven decision making. This is why we’ve created an online data analysis tool that enables clients to get the most out of their data, visualize it in a meaningful way and easily share these generated insights in stunning real-time dashboards to make better business decisions faster. However, the insights we provide are completely useless if, at the end of the day, these reports are ignored by the actual decision makers.
This conundrum prompted us to take a deep look: why are business leaders not using data driven decision making? And what should you be aware of to make sure your decisions are based on numbers, not feelings?
Now that we’ve outlined the foundations of getting your data driven decisions right, we’re going to dig deeper into things to avoid by drilling down into the common past mistakes of data analysts and business leaders. Through observing and absorbing these key points with the help of data analyst software, you’ll be able to ensure that your data driven decision making in business is consistent, results-driven, and centered on your goals at all times.
1) Quality of the data
First and foremost, the main reason usually invoked is data quality. Data quality is the condition of a set of qualitative or quantitative variables, that should be “fit for [its] intended uses in operations, decision making and planning”, according to an article written by author Thomas C. Redmann. A good data quality management (from the acquisition to the maintenance, from the disposition to the distribution processes in place within an organization) is also key in the future use of such data. Collecting and gathering are only good if well managed and exploited afterward, otherwise, the assets’ potential remains untouched and useless.
2) Over-Reliance on past experience
Over-reliance on past experience can kill any business. If you are always looking behind you, there is a real chance of missing what is in front. So often, business leaders are hired because of their previous experiences, but environments and markets change and the same tricks may not work next time. One of the most cited examples of this is Dick Fuld, who saved Lehman after the LTCM crisis. Ten years later he pulled out the same bag of tricks and, as the Wall Street Journal Reports, “the experience he was relying on was not the same as this massive housing-driven collapse.” The recent crisis was much more complex. Environments and markets constantly change and, in order to be a successful manager, one must combine past experiences with current data.
3) Going with your gut and cooking the data
While some managers naturally go with their instinct, there is a significant portion who first trust their gut, then persuade their researchers or an external consultancy to produce reports that confirm the decision that they already made. According to the Enderle article mentioned above, this was commonplace at Microsoft. Researchers were tasked with providing reports that lent credibility to the executives’ decisions.
4) Cognitive biases
Cognitive biases are tendencies to make decisions based on limited information, or on lessons from past experiences that may not be relevant to the current situation. Cognitive bias occurs every day, in some way, in every decision we make. These biases can influence business leaders to ignore solid data and go with their assumptions, instead. Here are a few examples of cognitive biases commonly seen:
- Confirmation bias – Business leaders tend to favor information that confirms the beliefs they already have, right or wrong.
- Cognitive inertia – The inability to adapt to new environmental conditions and stick to old beliefs despite data proving otherwise.
- Group Think – The desire to be part of the group by siding with the majority, regardless of evidence or motives to support.
- Optimism Bias – Making decisions based on the belief that the future will be much better than the past.
Managers need to recognize that we are biased in every situation. There is no such thing as objectivity. The good news is that there are ways to overcome biased behavior.
As a result, these businesses identify business opportunities and predict future trends more accurately, generating more revenue and fostering greater growth through data decision making.
3 Data Driven Decision Making Examples Of Success
Now that we’ve gained a clearer understanding of what it means to make a data driven decision as well as the importance of data driven decision making, we’re going to delve into 3 inspiring data driven decision making examples.
One of the most notable examples of data driven decision making comes from search colossus Google, according to an article written on smartdatacollective.com. Startups are famous for disbanding hierarchies, and Google was curious as to whether having managers actually mattered.
To answer the question, data scientists at Google looked at performance reviews and employee surveys from the managers’ subordinates (qualitative data). The analysts plotted the information on a graph and determined that managers were generally perceived as good. They went a step further and split the data into the top and bottom quartiles, then ran regressions. These tests showed large differences between the best and worst managers in terms of team productivity, employee happiness, and employee turnover. Good managers make Google more money and create happier employees, but what makes a good manager at Google?
Again, the analysts reviewed data from the “Great Manager Award” scores, in which employees could nominate managers who did an exceptional job. The employees had to provide examples explaining exactly what made the manager so great. Managers from the top and bottom quartiles were also interviewed to round out the data set. Google’s analysis found the top 8 behaviors that make a great manager at Google and 3 that don’t. They revised their management training, incorporating the new findings, continuing the Great Manager Award and implementing a twice-yearly feedback survey.
Walmart used a similar process when it came to emergency merchandise in preparation for Hurricane Frances in 2004, as The NY Times reported. Executives wanted to know the types of merchandise they should stock before the storm. Their analysts mined records of past purchases from other Walmart stores under similar conditions, sorting a terabyte of customer history to decide which goods to send to Florida (quantitative data). It turns out that, in times of natural disasters, Americans turn to strawberry Pop-Tarts and beer. Linda M. Dillon, Walmart’s CIO at the time, explained:
“By predicting what’s going to happen, instead of waiting for it to happen… trucks filled with toaster pastries and six-packs were soon speeding down Interstate 95 toward Walmarts in the path of Frances.”
Walmart’s analysts not only kept Floridians pleasantly buzzed on beer and Pop-Tarts during the storm, but also created profits by anticipating demand since most of the products sold quickly.
3) Southwest Airlines
A data driven decision holds an incredible level of value across all industries, but one sector widely-known to benefit from such insights is the airline industry.
Southwest Airlines executives utilized targeted customer data to gain a deeper understanding of what new services would be most popular with customers as well as most profitable.
In doing so, the airline discovered that by observing and analyzing their consumers’ online behaviors and activities, it could provide different segments of customers the best rates for their needs in addition to an exemplary level of customer experience (CX).
As a direct result of this emphasis on data driven decisions, Southwest Airlines has seen its customer base, as well as its brand loyalty, grow steadily year after year.
The Role Of Dashboards For Data Driven Decisions
When you have to make a data driven business decision, dashboards may play a crucial role. Having all the historical and current data on a single screen, with the possibility to interact and dig deep into single KPIs or generating an overview of a department or company, dashboards will enable a holistic outline of important information. To see this in practice, we will now take a look at some of the selected examples.
1) General management
C-level executives have to stay on top of their data. To be able to efficiently track information based on their strategies and goals, every manager concentrates on the actual revenue generated over a specific time-frame, compared to the target revenue, and with a clear visualization how it has developed (or not), like shown in this example:
It also shows the revenue based on customer level, and statistics related to the customer acquisition cost and the total number of new customers acquired. This can help every manager to successfully base their decisions on visualized data, making the process much faster and effective. A testament to why data driven decision making is important in today’s business world.
2) Online retail
In online retail, data collection is quite simple and plentiful. Different ways of shopping, access to reviews and online opinions made consumers more informed than ever. That's why having a clear overview of data is of utmost importance for small business owners, and large enterprises alike. In the example below, we can see how this would look like as an example with selected retail KPIs:
The total amount of orders, the average orders per customers, top sellers, and return reasons stats and figures can give you an overview of the consumers' behavior, why your merchandise is being returned, and which time of the year is your benchmark with the biggest amount of orders. That way you can base your future decision solely based on retail analytics data, and not on a gut feeling that could ruin your business strategy.
“Torture the data, and it will confess to anything.” – Ronald Coase
There’s no denying it – by harnessing data in the right way and measuring your success, you stand to propel your business to new and exciting heights.
Now that you have access to all of the key ingredients to make the best data decisions for your business, it’s time to put your plans into action. Remember – for maximum success, you must avoid taking the wrong approach to data driven business decisions at all costs. A failure to do so will lead to making choices with your gut, biases, or fostering a poor data culture within your organization.
At datapine, we’re 100% committed to helping you make the best data driven decisions for your business. Our solutions combine the very best business reporting software with a cutting-edge perspective towards evaluating your decisions to start seeing results.
To start your own path to successful data driven decisions, visualizing all your data in one place, and generating insights with just a few clicks, you can try our dashboard software for a 14-day trial completely free!