If you have been paying attention to things happening in the global business space, one thing that seems to be consistent is the constant reference to data. Data has been and will continue to be a big deal in almost every area of our lives. The reason is that it helps largely with improving decision-making, from the government to agriculture, pharmaceutical to logistics, marketing to production, entertainment to tourism, and robotics to space travel. The perception around data and its uses keeps getting valuable. Data is one of the bases upon which the quality of living and working is constantly improved. We could stop right there considering that we already stated the relevance of data to just about any ad and everything but there is more.

What is Data Analysis

To simply put it, Data Analysis is the process of working through lots of information. This information is usually filtered and broken down to be presented as numbers and figures, this way it becomes a lot easier to make sense of it and look for vital information that helps any business make better decisions.

Processes involved Data Analysis for your business.

The first thing you would need to understand when it comes to applying data analysis for your business is clearly defining what problem you are looking to solve or what questions you hope to answer. Now pay attention to the quality of questions you are asking before proceeding with your research and analysis. Like they say “Garbage in, garbage out”.

–Choose your tools and methods: The success or failure will largely depend on the method and tools you choose to conduct your findings and make an analysis. With the right questions in mind, make findings on what requirements will suit what you are trying to achieve. Doing this puts you at a 50% completion rate of the research and data analysis process.

–Gather your data: As earlier stated, data gathering is about asking the right questions. Equipped with this, reach out to the target audience and get responses to such questions. There are multiple formats in which these can be presented, such as surveys, direct interviews, questionnaires, observations, etc.

–Clean up your data. After gathering sufficient data via whatever method or methods you choose, you would want to sieve through and ensure that what you got is what you hoped for. Check for accurately imputed information, duplicates, and irrelevant information, and compile what you find useful.

–Analyze your data and interpret it. At this point, you will need the help of data analysis applications and software. Depending on your business needs and the resources available, you can go from Microsoft Excel to Python, Microsoft Power BI, etc to help with interpreting the data. Then pick pointers you need to help you get closer to answering the questions you had set out to answer.

–Visualize Your Data. This part has to do with communicating your findings with visual aids. This is most useful when trying to get everyone that requires this info on the same page and it makes clarifications a lot easier.

The different types of Data Analytics a Business can apply

–Descriptive Analytics. Descriptive analytics has to do with using current and history to understand what happened in a business. Anybody serious about their business always has a thing for keeping financial records. Financial records are always the first port of call when ascertaining the health of a business. So in using descriptive analysis, it becomes easy to diagnose a problem based on activities that have already occurred and are still occurring. Data derived here mostly seeks to answer the question “What happened?” and attempts to establish patterns.

–Diagnostics Analytics. Having established that Descriptive Analytics seeks to answer “What happened”, diagnostics analytics seeks to understand why it happened. The data gathered for this kind of analytics tries to identify causes and events in a business. An example will be, an eCommerce brand that sees revenue growth by as much as 50%. They have the numbers that show a steady monthly rise and in about 6 months, it has developed to achieve a 50% increase. They will try to understand what has changed or improved that could be responsible for such. They begin to make assumptions or draw hypotheses such as, ‘holiday season sales’, a tweak in the marketing materials’, ‘improved customer service’ etc. Then test out these assumptions to find out which exactly it is. When they establish causality, they proceed to replicate and improve it.

–Predictive Analytics. As the name implies, data derived in this type of analysis is used to predict what could happen in the future of the business, the industry the business plays in, or a wave of customer buying trends that is likely to happen. This is made possible by also using current and historical data to gain valuable insight on happening in the future. A wide variety of diverse businesses use this even without necessarily thinking hard about it. For instance, in Nigeria and most parts of the world, as the year draws to a close and Christmas approaches. It is common knowledge that consumer spending goes up. Businesses like Airline services, grocery stores, logistics, tourism, food, and beverages all adjust to meet the high demand that the season is known for. While some offer discounts, others hike prices. Research shows that over 50% of the profit made by businesses is recorded in the last quarter of every year and that is a rough example of how predictive analytics is used.

–Prescriptive Analytics. This type of analytics helps businesses define what courses of action they need to take. At the point of conducting this type of analytics, all the previous types “what happened”, “Why it happened”, and “What could happen” have been answered. Now the focus is “What do we do next”. Borrowing the example about businesses positioning for the festive period towards the end of every year clearly describes the various actions businesses take to get the most out of the period. Historical data for the average business has made it so that even layman or marketwoman understands what actions they are required to take whenever the season approaches. As for big businesses, it affords them opportunities to establish their brands or capture more market share. In other cases, pursue new business interests or explore new, under-tapped, or untapped markets.

Core business Areas where Data Analytics is applicable

Finance: One critical area of any business is its finance. Revenue generation and resource allocation are heavily dependent on this aspect of any business. It easily projects the viability of a business in the mid and long term. This is why businesses that publish financial reports are often required to put all concerned parties on the same page with regards to a businesses status. Data analytics in a business means that the business can easily spot revenue leaks, poor allocation and unethical allocation of funds, plan expansions with revenue projections, etc. and this enables informed business decisions. Financial analysts, accountants, and auditors play vital roles in this area of a business.

Marketing: Whether it’s traditional or digital marketing, there is exist a heavy reliance on data analytics. Marketing trends are constantly changing so is consumer behavior. As a market analyst, growth analyst, digital marketer, media personnel, and social media marketing, you need constantly make assumptions, test those assumptions, get your data, and understand what is happening in the business you serve or what industry changes. This allows for adapting content and marketing strategies to meet your target audience where they need to be met. Fortunately, there exist lots of analytical tools and services that make the gathering, compiling, and filtering of data a lot easier. Use them as required to your advantage and position your brand or business ahead of trends and competition.

Product Development: So let’s say you came up with an idea that could revolutionize an industry or a niche, it will be heartbreaking to jump right to shipping that idea as a finished product to the market without understanding if the market even wants your product. The process of product development entails researching the market, target audience, market segmentation, interacting with customers getting feedback. Data derived from research conducted in this area enables refining and improving the product. It also helps with making customer-centric products.

Product Engineering: Data analytics make engineering processes more efficient in cutting costs, improving output, and overall improving customer experience. Historical data derived from debugging sessions, product testing, and troubleshooting helps identify possible product lags and poor user experience. With data analytics, you want to answer questions about who is using the product, how often they will use it when they will use it. Data analytics techniques that apply in Product engineering vary from Regression Analysis, Sampling, and Expected Value. To get a proper grasp of what data analysis does in product engineering is to understand that it helps unlock value.

Courses On Data Analysis  

To get you up to speed if you choose to get into Data Analytics you can take any of these courses depending on the aspect of Data Analytics you wish to pursue or apply to your business;

-Business Analytics

-Data Visualization

-Data Analytics with Python

-Data Science

-Excel to SQL

-Basic Excel

-Machine Learning