Today, the whole management world talks about how to create a successful data-driven decision-making process in business to improve results.
Using data to make decisions is not just about the number of data scientists or technological components you have.
Data based business decisions arise from a complex process that involves people, relationships, analytics, culture, software, and problem-solving.
Wherever you work (in marketing, finance, banking, etc.), you must build a data culture to succeed in our information era.
On this page:
- What is data-driven decision-making? Definition, meaning, and importance.
- The steps of data-driven decision-making process and model.
- Characteristics of data-driven companies.
- Infographic in PDF.
Why data-driven decisions are important?
Data hold valuable insights.
Managers can transform those insights into decisions and actions that improve every aspect of the business.
Let’s see the definition.
Data-driven decision making is a process that includes making decisions based on data insights and facts rather than making decisions based on intuition, belief or observation alone.
Having data on-hand empower you to answer crucial business questions such as:
“why?”, “how to sell?”, “how to grow your business?”, “how to understand customers?”, “are customers going to buy your new product?”, “where to invest?”, “what differentiates your product?”, etc.
The data-driven companies gain a range of benefits such as: a better understanding of the market, greater customer acquisition and retention, lower costs, higher profitability, and improved pricing.
But what are the characteristics of data-driven companies? What business model they follow in order to maximize the value of the data?
Here are the key characteristics that they have:
- They understand the data very well. It means they know where the data came from, what is the quality of it, which are the best data analysis methods, how reliable is the data, how to measure its reliability, etc.
- They stimulate the ongoing sharing of information and collaboration. Everyone in the organization has access to the appropriate data.
- Keep their data clean. The data must be well organized, documented, and error-free. With the help of the right data cleansing software tools, companies make a good basis for decision making.
- They have the right set of tools and skills to make insights into structured and unstructured data and thus to come up with business decisions and make predictions.
- They work with real-time insights.
- They pay serious attention to data collection tools and processes as primary elements of the whole data environment.
- They constantly update and improve their skill sets.
- Data-driven businesses are able to apply the insights in a manner that supports business goals.
Companies are using data to make better decisions about everything you can imagine – from product creation and marketing to hiring.
So, how the data-driven decision-making process looks like. How to implement a data-driven model?
Step 1: Set clear goals
Before starting to play with data for decision making, you must have a clear idea about what you want to get.
What are your targets and objectives?
Do you want to make the market segmentation more efficient? Do you want to reduce financial risk? What kind of new product to offer? How to add more value to the current products? Do you need to optimize your supply chain?
A certain level of clarity about your goals will help you to convince others to support the project for achieving them.
Step 2: Choose the right data sources
Now, you need to decide which data sources to use in order to answer all those questions.
This requires checking the data that you already have and defining outside data sets with valuable information related to your problems and goals.
Today you have many ways to collect data. Most of these ways are free, innovative, and very powerful.
Consider internal and external data sources. Internal data is the data gathered within your company. External data is those collected outside of your company.
Internal data sources include:
1. Sales data. Examples of sales data are revenue, profitability, price, distribution channels, buyer personas, etc.
2. Finance data such as production costs, cash flow reports, amount spent to manufacture products, etc.
3. Marketing data such as customer profiles, level of customer satisfaction, customer engagement through content marketing, customer retention, etc.
4. Human resource data such as costs to recruit and train an employee, staff retention rate and churn, the productivity of an individual employee, etc.
5. Customer data from orders
6. Transactional data – can show you current and historical data relating to the shopping trends and behavior of your customers.
7. Your Customer Relationship Management System (CRM Software) – it is a great source of Information like clients’ company affiliations, regional or geographical details for customers, etc.
8. Business emails
9. Your social media profiles – examples of data that you can collect from social profiles include: likes, shares, mentions, impressions, new followers, comments, URL clicks.
10. Data from your website analytics such as visitor’s location, keywords used by visitors to find your site and business, most popular content, etc. Popular free platforms for insights into your website data is Google Analytics and Google Search Console.
11. Internal documents
12. Chatbot. If you have a chatbot, you can gather user data such as the user’s name, email, customer preferences, etc.
External data sources include:
1. Open Data Sources
These are some of the most popular free business data collection methods. Open data are large datasets that are free and available to anyone with an internet connection.
Governments, independent organizations, and some businesses are those who build open data for free access.
Open data can be many types – from public data collected and provided by government agencies to economic trend roundups from financial organizations and banks.
Popular sources of open data are: Data.gov, World Bank Open Data , Google Finance, Amazon Public Data Sets , Healthdata.gov , The International Monetary Fund , The National Center for Education Statistics , etc.
2. Social mention tools
Social media networks (Facebook, Twitter, Linkedin, etc.) are one of the biggest sources of data.
Social mention tools allow you to collect data on what your audience says about your brand, your competitors, and the market in general, across the web’s social media landscape.
3. Competitive intelligence tools
Competitive intelligence tools can be very powerful data sources. They provide you with critical insights on competitors.
Competitive intelligence tools allow you to collect, analyze, and understand data about your market that includes competitors, products, technologies, customers, and trends.
4. Online surveys
An online survey is a questionnaire that people can fill in over the Internet. Businesses of all sizes conduct online surveys to research their target audience.
With today’s powerful survey software tools, you can quickly create, collect and analyze surveys.
Online surveys are cost-effective and can save you a lot of time and effort. In addition, it is very easy and convenient for the target audience to complete surveys online.
5. Online focus groups
In an online focus group, a large group of pre-screened individuals logs in to a secure site or web conference where they are interviewed via webcam. The discussion is led by a person called a moderator.
The goal is gaining valuable insight for a company through spoken feedback and comments of consumers.
Used to gather qualitative data (see qualitative vs quantitative data), online focus groups allow you to collect information on anything from products and services to customer’s beliefs, perceptions, and opinions.
All of the above data sources can bring you critical insights on customers, market trends, competitors, business landscape, economical situation, etc.
Once you’ve looked through the list and consider which ones will fit your needs, spend more time researching each data source option.
You need to consider your goals, your data needs, the pros and cons of each source, the costs of data collection, etc.
Step 3: Set clear metrics
Stating the clear goals and defining data sources are great things. However, how do you know when you are close to achieving these goals?
With the help of the right metrics, of course.
Tracking some key strategic metrics is a crucial step toward a more data-based decision making.
Metrics are numerical values to help you find out whether your efforts are making a difference. If yes, in what direction.
For example, if your goals relate to improving sales, you might want to look at metrics such as the average time needed to close a deal, conversion rates, or revenue expectations.
Choosing the appropriate metrics is not a single and simple action. It is a process that involves choosing and refining initial metrics, defining the metrics that will have the most impact, choosing who will track the metrics, etc.
Step 4: Monitor your metrics on a regular basis
It is a vital moment. Monitoring metrics frequently makes the data useful.
Be able to access the metrics on every device: mobile, desktop, tv, etc.
Share it with your team members and the people involved in the data management process so they know how they’re progressing towards the targets and goals.
Aim to monitor real-time data, not only summarized values.
Step 5: Choose the right data dashboard
Data-driven organizations start to look at their data from the morning. It’s a habitual practice.
They look at the data in dashboards that describe key metrics.
These dashboards are most commonly implemented by a business intelligence software application with access via the Web.
A dashboard is a crucial tool in the data-driven decision-making process. It is the foundation of your data analysis.
There are two typical complaints about dashboards.
The first is that they don’t contain a sufficient amount of data; the second is that there is overly much data.
Make sure the dashboard you are going to work with, covers the basic BI dashboard best practices.
Step 6: Get the key people
As you are going to make decisions and achieve goals, you need to find out who is playing a role in each particular problem and goal.
Each project involves people in it.
For instance, if it’s a marketing project, you need to draw in the marketing head and the key members of the marketing team.
But you might also need to include members of the IT team or sales department.
Additionally, you might need to create roles for people with specialized skills and expertise who will take care of specific issues.
Step 7: Analyze your data
To analyze the data effectively, you need to choose data scientists, business intelligence specialists and to pick the right data analytics software tools.
The marketplace for data analytics software will offer you excellent tools for a variety of use cases and budgets. They connect all the different data sources.
Data analytics tools provide features such as beautiful dashboards, reporting capabilities, data source connectivity, robust analytics, and many other capabilities that will allow you to transform raw data into valuable insights in just a few clicks.
The central to a data-driven decision-making process is the role of the data scientist.
Data specialists have a set of data scientist skills that allow them to analyze and operate with data. The skills include statistical and mathematical knowledge, programming languages, infrastructure design, data visualization, etc.
The level of skills and software features will vary according to what you want to analyze.
Today businesses are constantly gathering more and more data on every dimension of their operations.
Knowing how to manage these data and using it for decision making is what distinguishes winners from losers.
With the help of a well-organized data-driven decision-making process and model, businesses can leverage their resources to provide relevant insights when and where they are needed.
Here is a great article about “34 business intelligence and marketing pros reveal their top tips for creating a data driven culture within an organization” from NGDATA.