Every business manager is a data analyst whether they realize it or not; they perform analysis every time they are looking for an answer to a question.
In your technology resume and in your technical job search, it is important to include your data analysis skills, and not just focus on your managerial skills. Think about the different types of analysis that you’ve done and the answers that you’ve sought. Some clear examples include forecasting, causal analysis, and cash flow management. Here are core examples of what data analysts examine:
- What happened?
- Why did something happen?
- What is likely to happen in the future?
- How can I change what I expect to happen?
Who are the Data Analysts?
Those who perform the role of a true data analyst often do so as part of larger roles and responsibilities. They don’t see themselves as analysts but as managers. If you are a front office benefits person, for example, you are working with people on a day-to-day basis, so you use data to look at one enrollment at a time, one person at a time. If you’re a manager, you look at aggregates and trends. You are asking yourself, such questions as “How many people are enrolled in this program?” and “What is the program expected to cost next year?” As the manager, you are doing data analysis work versus the person working on the front line who is doing record keeping and reference work.
Marketing managers analyze campaign effectiveness and customer behaviors. Financial controllers perform cash flow analysis, revenue analysis, and more. Compliance officers conduct risk analysis. Sales managers analyze pipelines and forecast sales volume and revenue. Customer support managers analyze customer satisfaction. R&D organizations perform competitor analysis and demand forecasting. HR analyzes employee retention, employee performance, regulatory compliance, and much more. Every good product or process manager does some kind of data analysis.
None of these people has the title of data analyst as defined by the business, and they’re often not the power users as defined by IT. They go quietly about their business–commingling data as needed from operational and analytic sources. Their objective is to resolve problems, identify conditions, and understand situations using the most direct and efficient means. The risk to the organization lies in not being able to identify or manage this valuable asset.
What does it take to be a Data Analyst and perform data analytics work?
Analysis is the process of breaking complex things into smaller, easier-to-understand pieces. It requires depth of knowledge about the subject being analyzed and the skill to break it into understandable pieces. It demands natural talent to understand each of the individual pieces, see the relationships between the pieces and appreciate how each contributes to the whole.
This combination of essential knowledge, the right skills, and natural talents is necessary. The three elements work together whether data analyst is your job or is part of a bigger job.
Data Analyst Knowledge is the understanding gained through education and experience. It is distinct from skills and talents and is generally broader in scope. The data analyst needs knowledge of:
- Business concepts and terms
- Business measurement principles
- Business metrics concepts
- Data analysis and design
- Business frameworks, such as customer relationship management (CRM), business performance management (BPM), and financial management
Data Analyst Skills are concrete abilities that are needed to perform tasks and produce results. They are learned and can be applied to produce solutions in a problem domain. The skills needed by a business analyst include:
- Business and data analysis
- Charting, graphing, and data visualization
- Analytic technologies such as OLAP, business scorecards, performance dashboards, and data mining
- Monitoring and forecasting related to business processes and results
Data Analyst Talents are the innate capabilities that a person brings to any endeavor. Although learning may enhance the ability to use talents, the talents themselves are inherent in personality, working style, and thinking style. Business analyst talents include:
- Pattern recognition
- Understanding of cause and effect relationships
- Inquisitiveness and the desire to understand “why”
- Separation of complex subjects in to understandable pieces
- Analysis and understanding of complex processes, logic, and subjects
To be a well-rounded data analyst, it is also important to have a good understanding of other areas within a data ecosystem. Examples include Information Management Fundamentals, Data Analytics, Data Quality, Data Governance, and Data Integration. For comprehensive online education, I recommend eLearning Curve.
Once you have the data analysis skills, it is easy to springboard to other data-related roles, such as data engineer and data scientist.
How do data analysts use their skills in performing data analytics work?
Knowledge and skills are essential for every data analyst. Analysts know how to use a variety of tools and techniques from spreadsheet macros and Excel PivotTables to data mining and pattern recognition. The distinguishing feature for analysts is not how they use their knowledge and skills, but how they apply their natural talents in combination with their other abilities.
A good data analyst is naturally inquisitive, likes to take things apart to see what makes them tick and is open to rethinking preconceived ideas. They are much like the annoying toddler who torments parents with a seemingly endless stream of “Why?” pleas. The desire to examine things and to dive into details makes them a natural choice for the role of data analyst.
Consider, for example, a marketing manager in the role of unrecognized data analyst. Analyzing click-through responses with OLAP, she knows which channels have historically had the highest response rates. With some slicing and dicing, she sees more detail by product type, product, region, and date.
Using this information, she plans marketing campaigns that mirror those with the best historical response patterns. On the surface, this seems like a logical way to make decisions and to plan marketing campaigns. OLAP works well to tell the manager what happened in the past and to help the manager make plans to repeat those successes.
Using OLAP to look at the past is the most common analytical technique available to data analysts. Historical analysis drives decision-making within many organizations. It also drives organizations to make the same decisions over and over again.
They lose opportunities for innovation and may be slow to respond when things change. The marketing manager, for example, won’t discover new market opportunities through OLAP. The very same manager, however, supported with data mining and predictive analytics capabilities, might make different decisions –- to explore, innovate, and adapt to change.
A data analyst equipped with the knowledge, skills, and talents to use data mining tools and techniques can see patterns in the data that suggest business opportunities. They understand what the patterns suggest and know why the patterns exist and what they imply. Their knowledge and skills provide the know-how to use the tools and understand the data. Their talents provide the insight for looking into the future. Recognizing the hidden data analysts and supporting them with advanced analytic capabilities simply makes good business sense.
A business that does not recognize, reward, and nurture everyone who analyzes its data risks losing a valuable asset. An IT department that does not provide appropriate technical services to those who perform the role of a data analyst risks being misaligned with the needs of an organization in its quest for informed decision-making.