IT & Technology

Analytical Thinking: How to Make the Most of Your Data

Data just keeps getting bigger. Understanding and using it effectively will vastly improve decision-making, adding value to your business and leading to success in your career.

Analytical Thinking: How to Make the Most of Your Data
Editorial Team
January 17, 2022
Analytical Thinking: How to Make the Most of Your Data

The amount of data we generate and consume is increasing by the day. When we watch a show on Netflix recommended to us by the streaming platform based on our viewing history, we are taking a data-driven decision. It's the same when we check the weather before getting dressed for work. By one estimate, we are set to create 463 exabytes or 212,765,957 DVDs worth of data in a day by 2025.

Data is an essential part not only of our personal lives but also our professional lives. In the workplace, data helps us improve customer experience, optimize internal processes, prepare strategy, innovate, and much more. However, data on its own is meaningless. To make full use of the vast quantities of data they collect, businesses must know how to draw insights from those data sets and act on those insights. To do so, they need leaders and employees with the ability to think analytically.

Analytical thinking in a data-driven world

The words "analytical" and "analyze" both come from the Greek verb "analyein", which means to "to break up, to loosen." Analytical thinking is to break down problems or tasks into smaller parts to find a solution or complete a job. A more detailed explanation of analytical thinking is that it involves collecting information and examining, visualizing, and using that information to solve problems or to come to a conclusion or opinion. Most importantly, analytical thinking allows people and organizations to take decisions and form opinions based on actual facts. There is a subtle difference from critical thinking, which means to use and analyze data to come to a conclusion or decision based on one’s best judgment.

Improve the quality of your thinking process and make better decisions with Learnit’s Critical Thinking class.

The science of analyzing raw data and using it to draw conclusions is called data analytics. It is one of the most in-demand jobs today with demand far outstripping supply. By the US Bureau of Labor Statistics’ estimate, the data science field is expected to grow at least 28% through 2026.

However, it isn't just data analysts and data scientists who work with data and need the expertise to do so. Given the massive data generation, growing automation, and rapidly changing skill requirements in today's workplace, employees across departments and roles need to work with data and develop analytical skills. The marketing department relies on data to monitor market trends and come up with great campaigns. The sales team uses data to track transactions. The product team uses data to better understand consumer behavior. The customer services group also banks on data to best serve customers. It goes without saying that all the employees on these teams need the literacy to effectively handle  their data.

Many people confuse data literacy with mastering data technologies and tools. Being data literate doesn’t only come when you become an expert in machine-learning. Rather, data literacy starts with familiarizing yourself with the concept of data itself. A good tip is to start with asking what kind of information is being collected by your department and what it can be used for. Once you are familiar with the basics of data and comfortable using it, it is that much easier to develop an analytical mindset to decipher that data and form conclusions from it.

Excel is a great tool for developing data literacy. No matter your current skill level, Learnit has Excel courses that will help take you to the next level. Check out Excel Introduction, Excel Intermediate, and Excel Advanced.

The data advantage: Fact-based decision-making

Today, businesses need not make critical decisions based on intuition and instinct as they once did. With data and analytical thinking, they have the tools to make informed decisions backed by solid evidence and not assumptions. By drawing the right insights from data, companies can predict future trends more accurately and pick strategies that have a better chance of success. However, 41% of companies struggled to turn data into decisions in 2020, says Massachusetts-based research and advisory firm Forrester.

What do employees need to do to use data effectively and develop analytical skills?

1. Lose the fear of data.

Most people feel overwhelmed and uncomfortable when presented with large amounts of information, especially numbers. They don’t know what to make of all that data. They are also afraid of using the wrong data, making a bad decision, and dealing with the consequences, which might be career-threatening. Overcoming that fear is the first battle.

2. Get comfortable working with data.

Ask questions whenever you have doubts. When your company makes a big decision, see what data backs up that decision. This will help you examine and interpret your data accurately when it is your turn to make a decision.

3. Get access to your company’s data.

If you don't have it, ask for it. Data access is a common problem and employees in many companies don’t have access to the most basic data.

4. Don't think of data as a technical issue.

True, making sense of data requires certain technical skills. But these skills can be learned (more on this later!)

5. Errors can creep into your decisions and make them ineffective

Finally, remember that even with the ability to think analytically, errors can creep into your decisions and make them ineffective. This is because when we analyze a particular data set, there is a danger that we might filter the information through our personal beliefs, biases, and experiences. Awareness of common biases is vital to polishing analytical skills. Learnit’s Unconscious Bias workshop can help you develop an awareness of common biases and learn how to disrupt biased behavior.

To infuse data and analytical thinking into decision-making, employers must make the following happen:

  1. Make sure employees have access to the data they require. It’s unfair to deny them access and yet hold them responsible for their decisions in the absence of that data. In many companies, there is a lack of transparency around where data is stored and who has access to it. Fear of security and confidentiality breaches makes some companies guard their data when it fact it should be shared and put to good use. Other organizations lack the necessary data protections and have too many privileged users who don’t actually need the access they enjoy. Investing in a simple yet secure data management system that gives access where required, insists on strong password protections, and complies with government security standards can take care of most of these concerns. Successful companies know how to appropriately share data with the right users and also have an airtight data security policy.
  2. It's not enough to ensure your company data is easily accessible to your employees. You also need to arm them with the skills and tools they need to extract, manage, and organize that data. Structured Query Language (SQL), a widely used programming language, is one such valuable tool. Even employees who are not data analysts or part of the tech department can benefit from some knowledge of SQL. To start with, you can teach your employees how to execute an SQL query, which is a request for data or information from a database table or a combination of tables. For example, "show me all the transactions for Customer X in 2021." With SQL, retrieving data from a database, inserting new information, updating and deleting records becomes easier. Most importantly, both you and your employees can be assured that the right data is being used to arrive at an important conclusion. Learnit offers very useful introductory SQL courses such as SQL Querying Fundamentals and Introduction to SQL Databases. 
  3. Pay attention to the quality of your data. A lot of the data sitting in the databases of organizations is stale and irrelevant. A Harvard Business Review study published in 2017 found that only 3% of companies' data met basic quality standards — a result that remained true in 2020, according to the researchers. Poor quality data leads to incomplete customer data, wasted marketing efforts, increased spending, and bad decision-making. By the researchers’ estimate, bad data costs a corporation about 20% of its revenue.
  4. Provide employees with learning opportunities so that they can become data-literate and pick up analytical skills. Basic data skills should be a fundamental part of a company’s learning and development program. For those new to using data, Excel is a great foundation. Learnit's Excel: Intro to Data Analysis workshop is a beginner's course that helps employees summarize, format, and visualize data. The program teaches participants how to clean up data, convert it into well-defined lists and tables, and visualize it using Excel functions like pivot tables and pivot charts.

In addition to learning key data analysis skills, employees also need to know how to present their data with their team and company stakeholders. No matter how good your data is, it cannot grab the attention of your audience without good visualization and storytelling. Learnit’s Power BI and PowerPoint courses will not only teach your employees to create impactful and sophisticated data visualizations but also build on those visuals by telling a powerful story, all for a better understanding of the data. The PowerPoint Psychology Tips course, for instance, covers both storytelling techniques and techniques for reporting information effectively.

  1. Encourage a culture of data-driven decision-making. It’s not enough to just amass large quantities of data, implement the latest technologies, and pay big bucks to hire talent. Companies must be committed to ensuring that every decision, big or small, is backed by data. This kind of commitment requires a change in mindset where employers and leaders are willing to let go of their habit of taking decisions based on instinct or, worse, guesswork. Secondly, managers and team leaders need to take the lead and make it clear that all decisions must be rooted in facts. A data-driven culture is near impossible if you keep your data scientists separated from the rest. To leverage the best value from the data at their disposal, data experts need to know what is happening in other departments. Staff rotations are an effective way of having them experience the challenges and problems their colleagues in other departments face. These colleagues will, in turn, benefit from having experts mentor them on working with data.

Explore Learnit’s Analytics and Data classes.

Data management best practices

  • Check your data for accuracy and relevance. This is one of the biggest challenges today. The Covid-19 pandemic has made a lot of historical data and analytics models dependent on this data obsolete due to the emergence of new consumer behaviors. Making decisions based on out-of-date data leads to errors. When working with data, it is natural to be deeply immersed in each number or statistic. We often forget to take a step back and look at the data in its entirety, which can be a costly mistake.
  • Just like how most data has an expiry date, data techniques also keep changing. Keeping pace with these changes is vital to an organization’s decision-making and overall success. For example, corporations have sworn by big data for many years, but this is now changing. Gartner predicts that by 2025, most companies will have moved on from big data to small and wide data. Small data involves applying analytical techniques to draw insights from small, individual sets of data. Wide data allows you to examine and combine large and small, structured and unstructured data.
  • Working with data can get chaotic and there is plenty of room for errors and inconsistencies to creep in. Organizations can skip the chaos by keeping their data management systems simple and easy to operate. An efficient and effective system should spell out the responsibilities of each team (IT, data engineers, system administrators, etc) with access to or control over the data. Universal access might save everyone a lot of time but typically isn’t the wisest choice. Instead, experts say limited permissions that allow people to access most of the data they need most of the time, with room for dealing with access problems as and when they crop up, is a more secure data governance method. 

Database management is a valuable skill and Learnit’s Access Introduction course — targeted at data analysts, database users, managers, and just about anyone who uses Microsoft Access — will help you sort, filter, and enter data and create entire databases.

  • Make sure everyone in the organization is up to date on best practices related to data collection, entry, management, security, compliance, and privacy. Holding annual enterprise-wide data governance meetings is one way of confirming that everyone is aware of and on the same page regarding the company’s data practices and rules. Another way is to put in place a data governance policy that is easily accessible and acceptable to all employees.

To develop a mindset that makes data-driven and data-supported decisions, invest in Learnit’s Drive Decision Making: Develop a Data Mindset workshop for you or your employees.