In this article we will discuss:
What is data-driven decision making
It is a strategic approach that uses data as a base to make informed business decisions. This approach is also referred to as DDDM or data-driven decision making. It involves collecting data based on measurable goals and/or KPIs. The data is then analyzed in order to identify patterns and generate useful insights.
Companies then leverage those insights to develop strategies for business growth making data-driven decisions to achieve goals instead of intuition.
Which types of data analytics are used for decision making?
There are 4 types of data analytics you can use to improve your business decisions.
Means using raw data to describe a given/current situation. For example, monthly sales or conversion rates for a specific period. Or a demographic analysis of customers. Data mining and visualization are some techniques used for this type of data analytics practice.
Seeks to find out the “why”, identifying patterns, and analyzing data to understand why something discovered in the previous step is happening. Business Intelligence [BI] dashboards use this technique to understand the root cause of issues in one’s organization.
Analyzes past and present data in order to forecast what is to come. It allows companies to predict future sales, revenue, and market changes. For this type of analytics, data scientists use data modeling and machine learning.
This technique involves taking the findings of the previous three and using it to deliver value, by determining the possible solution to a problem. For example, prescriptive analysis is what your mobile GPS application uses to suggest the best route to reach your destination.
What are the benefits of a data-driven approach for decision making?
Basing your decisions on data means that you are relying on accurate information instead of on your gut feeling. In terms of benefits, perhaps the most prominent is a mitigation of risk. When you base your decisions on cold hard data, you put yourself in the driver’s seat in terms of the risks vs benefits of each decision you make.
Let’s take a look at an example. Imagine you are launching a new product and are planning the marketing campaign. Instead of basing your strategy entirely on your current market research, you can collect data on and look at what worked in previous similar product launches. This will essentially enable you to reach smarter conclusions within a shorter period of time.
According to research, leveraging big data analytics for decision-making offers several benefits:
- Better strategic decisions (69%)
- More control over the operational processes (54%)
- Improved understanding of customers (52%)
- Cost reductions (47%)
Today, data analytic tools and techniques are delivering value across industries, and what was once cutting-edge is now a necessity in order to stay competitive.
How do you get started with data-based business decisions?
It is important to have a clear plan of action that will reflect what type of data you need, how to find it, and what outcome you expect from it. Put simply,
What do you want to measure and why?
If you don’t know where to start, below you can find a simple 4-step process that you can use in order to get started:
ONE: Know your goals and priorities
What is the issue you want to improve? Check your business goals and your priorities. For instance, let’s say you want more clients buying a specific product that has had weak sales to date. First, you want to decide which data is crucial to understand the drop in sales, for example, consumer search trends, spending habits on marketplaces, and consumer sentiment on social media. Once you have these, you can move on to analytics, reaching conclusions and concrete business actions to improve sales volume (advertising to new audiences or changing your offering based on current consumer preferences, for example).
TWO: Find and present the data
Once you identify the problem you want to solve and identify the relevant points, then a plan for actually collecting this data needs to be put in place.
You will most likely want to use a combination of web scraping tools to collect your target data together with analytics tools which can help you derive insights. You will want to use data collection tools specifically geared towards your needs such as a search engine crawler if search trends are where you think your biggest opportunities lie, for example. How you present the data is also essential. The right visualization techniques can help you reach the insights you need at a glance. The wrong one can create further chaos and won’t help find the connections you are looking for.
Pro tip: You will most likely want to use a combination of online data collection tools to collect your target data together with analytics tools which can help you derive insights.
THREE: Draw insights from the data
Once you get the data you need, you can use analytics tools to help you identify patterns, connections, and trends. For example, if you realize that certain keywords are trending with your target audience you can work to add these to your relevant web content.
The insights you get will help you create an action plan based on accurate and up-to-date data.
FOUR: Monitor, measure, repeat
Once you reach a conclusion, decide what actions you want to take based on your data-driven insights (maybe products with free shipping sell best, for example, and you want to offer this on certain products and see the effects this has on sales). But this is of course not the end of the road. This process should run in a loop enabling you to continuously improve your business processes. Monitor your improved strategies and measure again to confirm the success or failure of your strategy. Then repeat the process with all other business units.
This is the best way to incrementally build a data-driven company.
3 things to consider for better data-based decision making
It is easy to get overwhelmed or act based on predetermined bias when working with data. The problem is that these kinds of mistakes lead to inaccurate data and ultimately negatively affect the outcomes of your strategy. Here are three things you should take into consideration when navigating a company based on data:
Look out for biases
Sometimes we see the things we want to see. This is one of the great challenges of data analytics. Adopting a data-driven culture means that data is accessible to the right people enabling them to make better-informed decisions. You can eliminate biases by cross-referencing data from different sources or by collecting data via a wide variety of peers, and devices as well as from different GEOs when that does not conflict with your goals.
Gather data from the get-go
Most companies wait until they have a perfect business/product/marketing strategy in place before they begin collecting data. While this is usually more true for larger companies, smaller businesses and startups tend to be more flexible. You should use this agility to reap the benefits of data collection from the very beginning. For example, a startup that begins its product-to-market strategy using data will be able to find a product-market-fit by first conducting accurate market research using data (where is audience interest? What are competitors offering? etc).
Set measurable and attainable goals
Many businesses can get very excited about being able to collect large amounts of data which can lead them to strategic insights and business success. But many businesses have been destroyed by over-ambitious projects, for example crawling all target audience profiles on social media. I recommend that you decide which goals you would like to achieve and divide them into more bite-size portions. For example, which women’s shoe posts have the most engagement (likes/comments) in Paris. That will make it easier to understand where current interest may lie in this GEO and you can take concrete actions right away (for example run an ad campaign tailored to your newly discovered target audience).
Summing it up
Creating a data-driven company culture has its challenges. These may surface both in the form of resistance from colleagues as well as the difficulty of sourcing, analyzing, and maintaining a data-driven business machine. But despite these hurdles, building a company that thrives on data is an integral part of knowing your audience, knowing your competition, and the landscape within which you operate. Making high-quality data collection a business imperative for the coming year can give you the market advantage you have been looking for.