In a common parlance, Big data analytics is the often complex process of examining large and varied data sets, or big data, to uncover information — such as hidden patterns, unknown correlations, market trends and customer preferences — that can help organizations make informed business decisions.
The practice of Data Analytics has gradually evolved and broadened over time, providing many benefits and Nigeria is not in isolation.
The use of Analytics by business can be found as far back as the 19th century, when Frederick Winslow Taylor initiated time management exercises. Another example is when Henry Ford measured the speed of assembly lines.
In the late 1960s, Analytics began receiving more attention as computers became decision-making support systems. With the development of Big Data, Data Warehouses, the Cloud, and a variety of software and hardware, Data Analytics has evolved, significantly and Nigeria is not left out. Data Analytics involves the research, discovery, and interpretation of patterns within data. Data Analytics is in four major categories as enumerated below:
1. Predictive Data Analytics
Predictive analytics may be the most commonly used category of data analytics as it is used to identify trends, correlations, and causation. The category can be further broken down into predictive modeling and statistical modeling. But, it’s important to know that these two really go hand in hand.
For instance, using an advertising campaign on Facebook for baked goods. Statistical modeling could be used to determine how closely conversion rate correlates with a target audience’s geographic area, income bracket, and interests. From there, predictive modeling could be used to analyze the statistics for two (or more) different target audiences and provide you with possible revenue values for each demographic.
Business applications of descriptive analysis include:
- KPI dashboards
- Monthly revenue reports
- Sales leads overview
2. Prescriptive Data Analytics
Prescriptive analytics is where AI and big data meet to help predict outcomes and what actions to take. This category of analytics can be further broken down into optimization and random testing. Using advancements in machine learning, prescriptive analytics can help answer questions like “What if we try this?” and “What is the best action” without spending the time actually trying out each variable. Basically, it can help you test the right variables and even suggest new variables with a higher chance of generating a positive outcome.
Business applications of predictive analysis include:
- Risk Assessment
- Sales Forecasting
- Using customer segmentation to determine which leads have the best chance of converting
- Predictive analytics in customer success teams
3. Diagnostic Data Analytics
While not as sexy as some of the future data analytics, past data analytics serve an important purpose in guiding the business. Diagnostic data analytics is the process of examining data to understand cause and event, or why something happened. Techniques like drill-down, data discovery, data mining, and correlations are often employed.
In particular, diagnostic data analytics help answer why something occurred. Like the other categories, it too is broken down into two even more specific categories: discover and alerts and query and drilldowns. Query and drilldowns are what you’ll use to get more detail from a report. For example, let’s say that one of your sales reps closed significantly fewer deals last month. A drilldown could show fewer work days, reminding you that they had used two weeks vacation that month explaining the dip.
Discover and alerts can be used to be notified of a potential issue beforehand, such as alerting you to a low amount of man hours which could result in a dip in closed deals. You could also use diagnostic data analytics to “discover” information like who the best candidate for a new position at your company is.
Business applications of diagnostic analysis include:
- A freight company investigating the cause of slow shipments in a certain region
- A SaaS company drilling down to determine which marketing activities increased trials
4. Descriptive Data Analytics
Descriptive analytics are the backbone of reporting—it’s impossible to have BI tools and dashboards without it. It addresses your basic how many, when, where, and what questions.
The final type of data analysis is the most sought after, but few organizations are truly equipped to perform it. Prescriptive analysis is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision.
Prescriptive analysis utilizes state of the art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.
Artificial Intelligence (AI) is a perfect example of prescriptive analytics. AI systems consume a large amount of data to continuously learn and use this information to make informed decisions. Well-designed AI systems are capable of communicating these decisions and even putting those decisions into action. Business processes can be performed and optimized daily without a human doing anything with artificial intelligence.
Currently, most of the big data-driven companies (Apple, Facebook, Netflix, etc.) are utilizing prescriptive analytics and AI to improve decision making. For other organizations, the jump to predictive and prescriptive analytics can be insurmountable. As technology continues to improve and more professionals are educated in data, we will see more companies entering the data-driven realm.
However, Data Analytics in Nigeria is becoming popular and it’s receiving attention in most industries such as Banking, Insurance, Oil & Gas, Telecoms, FMCG and host of others. Octave Analytics is taking the lead in this regard for over three years now in areas of Analytics outsourcing for companies and industry specific training likewise coming up with innovative analytics model in addendum to the current once. Data Analytics in Nigeria is usually mismatch with the use of Power BI, Python, R , Tableau and host of other tools in silos.