A century ago, company owners relied on their experience and intuition to organize supply chains, adjust production processes, and gain new customers. Today, 65 percent of stakeholders agree that the complexity of business processes has grown dramatically, and important decisions must be substantiated and made within limited timeframes.
Clearly, companies cannot rely on unmeasurable decision factors anymore and require accurate intel to function correctly.
This is where data analytics for business comes into play. It helps stakeholders make more informed decisions about security, marketing, customer acquisition and retention, risk management, and process optimization. Seventy percent of companies that had implemented business data analytics as a part of their daily operations reported faster decision-making along with better financial performance and lower risks.
The problem is that despite the benefits, many businesses are still struggling to achieve data-driven decision-making (DDDM). Let us see what it takes to implement it in your organization.
What is DDDM?
Data-driven decision-making implies that you use metrics, numbers, statistics, and other hard data related to your business to make decisions. Analyzing this information for patterns gives you valuable insights on what you should do next, what is likely to happen, and why processes occur one way or another.
DDDM allows all lines of business to refine their strategies and improve performance and is widely used by industry leaders. Here are some examples:
- Lufthansa reported a 30 percent increase in operational efficiency due to business data analytics
- Google uses Project Oxygen to distinguish the traits of high performers and then develops training programs for employees based on the acquired information
- Amazon analyzes the behaviors of its customers to power its recommendation engine
But if DDDM is so good for business, why don’t enterprises implement it across the board?
Despite billions of dollars being invested in implementing data analytics for business, 70 percent of such initiatives fail. Here are some common obstacles many companies face:
To avoid these problems and put your business intelligence and data analytics to good use, it’s important to understand how to integrate them into your decision-making processes. Let’s look at some of the data analytics best practices.
How to implement data analytics for business
DDDM requires not only a complete rethink of working with information but also significant organizational transformations. We would suggest you approach this initiative from two angles:
- Preparing your data for insight extraction
- Promoting data-driven culture within your enterprise
This isn’t a full list of measures, but it does give you a starting point.
Organizing your data
Identify and prioritize your business goals
Companies tend to use only 45 percent of the business data analytics they produce, and the rest is ignored. This is ineffective. To make accurate decisions, not just any info will do, it has to be relevant.
When formulating your business objectives, ask yourself what you want to achieve and why. Goals set in a specific and quantitative manner let you select suitable indicators to measure your progress. In turn, these metrics will help you determine what data is essential for analysis regarding your goals.
In brief, instead of gathering all the data you can, we recommend you follow the formula: based on X, collect Y to answer Z.
Democratize your data
Data-driven decisions should be enabled across the whole organization, not just at the senior management level. Today, considering the ubiquitous volumes of information (74 trillion gigabytes of data have been created in 2021 alone), it is crucial to streamline the delivery of relevant data ready for analysis to decision-makers. This is called data democratization, and to facilitate it, you need to design new data supply chains, reducing the possibility of trapped data, silos, and bottlenecks.
Clean the data you’re working with
First, you need to identify the relevant data that you already have. Next, you need to do a cleanup.
It is also important to reduce data biases during this step. A bias implies that certain dataset elements are represented more heavily than others, which results in inaccurate analysis and poorly informed decisions. A typical example is the so-called filter bubble when personalized content algorithms supply users only with the information that conforms to their beliefs and resembles the previously consumed info.
Analyze data and make conclusions
There are many ways to develop insights from the acquired information. You can start by answering the question, “What has happened?” (Descriptive analysis) and move to scrutinize why it has happened (Diagnostic analysis) and what is going to happen next (Predictive analysis). Other methods include text analysis, data mining, image analytics, data visualization, and more.
Promoting data-driven culture
Extracting valuable insights from business data analytics does not guarantee success in implementing DDDM. Your biggest challenge may be resistance to change.
Surprisingly, companies do not embrace every beneficial change automatically. Reluctant corporate culture manifests itself on different management levels, and you might need additional effort to overcome it. Here are some ways to do it.
Organize support for your DDDM initiative
Change is often implemented from top to bottom, and C-suite is your friend when transforming corporate culture to make it data driven. Create a simple but illustrative POC to demonstrate the positive impacts that a data-driven approach will have on internal operations. Ideally, senior management should not just declare support but actually practice data-driven decision-making. Organizing a center of excellence to guide the DDDM initiative could also be a good idea.
Encourage and develop communication between leadership and data scientists
A common problem for many companies is the gap between senior management and data scientists. When there is no communication between them, two things happen:
- Business data analytics is detached from real business needs
- Leadership fails to extract the actual value from the acquired information
Communication allows you to merge the strategic view that management has with the technical perspective of your data analysts. As a result, analytics becomes more aligned with the company’s business goals, and leadership gains access to valuable insights.
When a data scientist looks at graphs and dashboards, they see patterns in business processes, nuances of operations, bottlenecks, and whatnot. When an unpracticed manager looks at the same graphs, they see numbers and colorful lines. Offering training and basic education is crucial for building a culture where each worker can make data-driven decisions.
Data is the “new oil” and putting it to good use can give you a significant advantage. However, changing the way your business works and setting it on the DDDM track can take a long time, and the need for accurate insights is often pressing. If this sounds like you, we have a relevant solution for you. Keep implementing DDDM at your enterprise while TEAM International collects, structures, and analyzes data to help you maintain the competitive edge. Feel free to contact us today for a free consultation.