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Businesses today are processing an enormous amount of data and intelligence. Whether you are a small business or a large enterprise, you need data regarding customers, sales, digital platforms, market, competition, supply chain, products, or services to give you the insight needed to further evolve your business.
Due to the sheer volume and speed at which data is available today, it is becoming overwhelming for many businesses to manage the information effectively with their traditional business intelligence (BI) programs. Therefore, a great number of opportunities are being left on the table.
The hot field that everyone is talking about, advanced analytics comprises skills and practices which analyze the data to acquire insights. This involves using technologies and extensive statistical methods and modeling in order to drive business planning and to make future improvements.
Traditional BI provides answers to questions such as what happened, how often, where the problem is, and what actions are needed. For example, we can figure out that sales increased from last month, by x amount of units sold. Based on this we can assess if we should expand to new markets, increase product lines, or re-evaluate the current process.
Advanced analytics take that a step further by also concentrating on questions like why is this happening, what if these trends continue, what will happen next, and what should a company do. With advanced analytics, businesses gain richer insights from multiple sources to reveal hidden patterns and relationships. The promise of becoming a data-driven organization brings a great return on investment by enabling businesses to bring efficiencies, lower operating costs, optimize processes, improve product and customer satisfaction and increase revenues.
Therefore, it’s vital for the continued success of a business to harness the power of data extensively to become more responsive, competitive, and innovative.
There are four types of analytics but it’s important to understand that they are not mutually exclusive. In fact, they complement each other and many analysts consider them to flow in a progressive order. However, most companies largely focus on the first two types, Descriptive and Diagnostic, which answer to the what and why, whereas it’s the latter two, Predictive and Prescriptive analytics, that provide the most crucial insights for a business to stay competitive. Let’s take a closer look at the four types of analytics.
1. Descriptive analytics answer the ‘What happened?’. As the name suggests, it analyzes the raw data of the past and gives it meaning. The past could refer to something that occurred a week ago or two years ago. Through the use of data aggregation and data mining, it allows us to learn from past behaviors, and see how the past interactions can impact the future results. Descriptive analytics looks at a variety of metrics like web traffic, monthly sales, average dollar spent per customer, inventory levels, top product lines sold and provide insights around the company’s production, finance, sales, operations, and customers.
2. Diagnostic analytics try to understand ‘why something happened’. This is the natural follow-up to Descriptive Analytics. After the ‘what’ should come the ‘why’. A very simple example might be a drop in web traffic month over month which is reported by descriptive analytics. Diagnostic analytics will use tools and statistics to tie the various sets of data to understand why the drop might have occurred. Traffic from previous years will be analyzed which could reveal a seasonality factor, thereby providing a diagnosis of the traffic drop.
3. Predictive analytics predicts the probability of something happening in the future or in other words answers the ‘What is likely to happen’ question. It identifies past patterns and uncovers relationships between different data sets by using various techniques such as data mining, statistics, modeling, machine learning and artificial intelligence. The outcome of predictive analytics is the probability and likelihood of-of future events, risks and opportunities. Examples of predictive analytics include forecasting customer behavior and purchasing patterns, inventory levels, sales activities etc.
4. Prescriptive analytics is a relatively new field of analytics which tries to answer the question ‘What should we do about it’. Prescriptive analytics uses extensive statistical methods and tools to prescribe a number of different potential actions and offers guidance on the best course of action. Prescriptive analytics does that by quantifying the effect of future decisions which allows companies to assess the options and decide on the optimal solution. In other words, companies get the ‘advice’ on how to optimize for future events. Some of the techniques and tools used for prescriptive analytics are a combination of business rules, algorithms, machine learning and computational modeling. These are applied to many different data sets including historical and transactional data, real-time data and big data. Prescriptive analytics are relatively harder to implement and most companies are not currently using them. However, when prescriptive analytics are applied and executed properly, they can have a very significant impact on the company’s earnings and revenues.
It’s not enough to simply invest in advanced analytics. Savvy organizations are also focussing on how to bring those analytics to the end business user. Sharing data in an easy to digest and visually powerful manner can transform and elevate simple insights from numbers into action. The intent is to enable the business users to get a quick delivery of the analytics and inform their business planning.
Organizations today are doing everything possible to drive business planning and to be more competitive. Given the vast amount of both structured and unstructured data available, advanced analytics leverages tools and statistics to glean actionable, powerful insights from the data and offer a future best course of action. It is equally important that the analytics are able to be shared and collaborated upon quickly and easily by all the business users in the company via dashboards. Having an analytics first mindset will enable an organization to stay ahead of the game.
