You can’t succeed as an entrepreneur in today’s business landscape if you still make important decisions based on gut feelings, hunches, instincts, or uninformed beliefs. All these methods are susceptible to unconscious bias and prejudice. To maximize your chances of success, you need to make decisions that are founded on data and facts. You need to set measurable business goals and key performance indicators (KPIs) and then use data analytics to track those KPIs. Data analytic tools use artificial intelligence (AI) and Machine Learning (ML) to collect, process, and analyze data with respect to your set KPIs. The tools then turn raw data into insights and knowledge that you can use to make the best possible decisions.
For effective data-driven decision-making, you must first effectively manage, govern, and visualize data. You’ll do that by following these 5 top strategies for data-driven decision-making:
Strategies for Data-Driven Decision Making
1. Only pick and focus on the data you actually need
Most of the data your data collection tools and software hand to you is just fluff. You don’t want to waste your time and money trying to make sense of the fluff. That’s why your need a clear, foolproof strategy for weeding out all the data your business doesn’t need. To create and actualize this strategy:
- Be clear about your business goals and KPIs. Know what you are looking for and what you need the data to do for you. You may, for example, want to know how successful different marketing campaigns and content marketing strategies yielded over a given period. Let that be your only focus for now. Bottom line: Know what you want so you don’t end up chasing multiple rabbits and catching none.
- Identify the key data sources. Data flows from different sources and directions, but all sources aren’t created equal. You may, for example, want to focus only on data coming from your website if your goal is to increase your online traffic and conversions.
- Always compare the insights you get from your data analysis with what other players in your industry are doing. That will help you identify areas where you can outsmart or outmaneuver the competition in order to stand out. Focus on those areas.
- Ditch your beliefs. Don’t allow your prejudices and unconscious biases to condition your vision of reality. If possible, have a team of professionals that advises you on which data the business needs at any particular time.
2. Leverage HIPPOs
As much as you should rely on data to make decisions, you must know that data isn’t always accurate. Some statistics and numbers that machines compute can be misleading because they lack the much-important human touch. To set your business apart from the competition, you need to analyze all data sets with the help of the Highest Paid Person Opinion (HIPPO). The highest-paid person in a team is in most cases the most experienced, assertive, knowledgeable, innovative, and/or skilled in the team. They have seen the wrong data before. They have made mistakes before. They can tell accurate data sources from a pile of inaccurate sources. They can tell when presenters exaggerate data to make it more exciting.
Bottom line: Having HIPPOs at the decision-making table will help you avoid the costly pitfalls of data inaccuracy.
3. Make data collection as simple as possible
Keep everything simple: Pick the best-specialized data analytic tool you can find and stick with it. Don’t be tempted to coopt every tool you find in the market into your decision-making process. Too many choices will only complicate the process, confuse you, and throw you off balance.
While at it, make data management as effective and as simple as possible. You can, for example, task heads of departments with the responsibility of managing data and conducting simple analytics. That will give them a well-rounded view of the business and the industry. Such department heads will always bring invaluable opinions and ideas to the decision-making table.
4. Organize your data for easier analysis
First of all, prioritize the data you collect to avoid data overload and the confusion that might precipitate. Data prioritization helps you to focus on the most important goals first and achieve the bigger goals faster. Secondly, you need to organize your data in clearly labeled and easily retrievable databases to improve data visualization. Optimal data visualization is when you see all your relevant data in one place and can easily connect different databases. It is also important that you have the luxury of downloading or uploading data from/to the database using any device and from any location. One way of organizing your data is having an executive dashboard that gives you quick access to all available databases.
5. Apply your data insights
Before doing anything else after completing your data analysis, go through the reports and conclusions to find mistakes and inaccuracies. Catching inaccuracies early means that you only base your decisions on the right metrics, which increases your chances of making the right decisions.
When done, present the data visually for everyone on the team to understand. Proper data visualizations will help team members to identify patterns and draw conclusions based on set KPIs. That will also help you promote a data-driven culture in your organization.
Knowledge Graph and Other Tools Necessary for Data-Driven Decision Making
Any set of data has interlinked facts and relationships. You need to identify these facts and relationships if you’re to make sense of your data and make the most of it. A knowledge graph helps you with that by using machine learning to:
- Define real-world entities associated with your data.
- Display the relationship between data points.
- Provide a platform upon which you can infer new and derived knowledge.
- Provide context to raw, unstructured data.
- Provide computational efficiency for an effective generation of data insights.
Bottom line: You can rely on knowledge graph machine learning to solve complex problems in data analysis. It helps you streamline, connect, and give meaning to unstructured and semi-structured data sources.
Other tools for data-driven decision-making:
i. Data collection software
A good data collection tool should be easy to use for your staff or research team. It should accurately track important information. It should be easy to integrate with other apps. It should have at least one standout feature that gives your business an edge over competitors. Lastly, it should be relatively affordable to buy, install, run, and update.
ii. Database management tools
A good database management tool should make it easy to create, update, modify, or retrieve information in your databases as required. Most importantly, it should keep your data guarded and safe.
iii. Microsoft Excel
MS Excel is a simple, versatile, and powerful data analysis tool. You can do a lot with its spreadsheet function; this function allows you to manage and organize large data sets. MS Excel also comes with inbuilt statistical analysis, graphing, visualization, and computing tools that you can leverage for simple data analysis.
iv. Advanced Data analysis tools
- SAS for statistical analysis and predictive analytics.
- MySQL for relational database management.
- Tableau for data visualization.
The future of business decision-making is anchored on big data. If you haven’t started collecting, storing, analyzing, visualizing, and drawing conclusions from data, you are missing so many important insights. It’s time you took data-driven decision-making seriously!