Data analytics is the science of analyzing raw data using various techniques and processes to make conclusions about that data, and eventually package it so that it can provide insights. These techniques can reveal trends, patterns and metrics that would otherwise be lost in the mass of information that is generated by a business in their daily operations. Companies can use these valuable findings to inform and improve how they do business, engage with customers, and make decisions related to their products and services.

Analytics is the process of consuming and transforming data so that it answers important questions about your business. Data analytics companies follow a clear process when conducting data analysis and rely on the analytics maturity model (AMM) to identify and present valuable information, helping organizations to improve their internal and external processes. The analytics maturity process involves an initial stage of data evangelism, followed by putting together the infrastructure, and eventually conducting various levels of analytics.

The process isn’t necessarily linear and when it comes to conducting analytics, very often multiple iterations are required to ensure that the questions being asked have been answered. Below we discuss the analytics maturity process in greater detail to provide greater insight into what each of its stages entails.

Understand An Organization’s Needs

Before beginning data analysis of any kind, data experts will typically begin by getting buy-in from all key stakeholders within an organization to adopt the importance of data and analytics. From there the goal is to understand the organization’s strategic goals and objectives of the data analysis. It’s crucial to understand which types of insights will best benefit the organization and inform their decision-making.

Identify the Problem, Ask the Right Questions

Asking the right questions is key to ensuring that the data works for an organization. Once an organization’s core objectives are outlined, and the problems are clearly defined and identified, data experts will consider which questions will need answering in order to achieve the organization’s mission.

This is one of the most important steps of a data analysis, as it shapes the very foundation of the process. Even with the most advanced IT software at an organization’s disposal, the analysis and insights drawn from the data is going to be only as good as the questions that are trying to be answered.

In analytics, the goal is to determine which questions we are trying to answer with the data, and analyze the relevant data while determining potential insights via reviewing and organizing the data in the process.

Define the Methodology and Set Research Priorities

Next, data experts will define the methodology and research priorities — in other words, what to measure and how to measure it. This step provides further guidance on what kind of data is needed to address the defined problem, and to answer all related questions outlined in the previous step.

After giving the methodology real direction and knowing which questions need answering, the most valuable and relevant data sources are identified, and collecting insights can begin.

Prepare Data

Enterprise Data Warehousing (EDW) is an integral part of analytics, and critical to data preparation and verification prior to undertaking advanced analytics. Data experts will load the data, prepare and verify it, and eventually undertake reporting and analytics. The three main steps in the data lifecycle in an EDW that pertain to data preparation and verification, prior to reporting and analytics include:

• Data Loading. Data is exported from source systems and loaded onto the warehouse, typically using bulk export methodologies such as CSV-based exports or API-based methodologies. In this phase, each source system gets its database or data set, logically separate from other systems and sources within the EDW.
• Data Preparation. The data preparation phase typically consists of three steps: data consolidation, data transformation, and application of the model. At this stage, data is consolidated from multiple databases into one, the raw data is then manipulated based on agreed upon business logic, and the incoming data is then injected into the data warehouse, into the eventual data model.
• Data Verification. While approaches to data verification often vary significantly between institutions, the ultimate goal of the effort is to verify that the data is correct and consistent with historical trends for each data field.

Analyze Data

With analytics we move from basic reporting to helping the business answer questions on their data, progressing from information to optimization. This stage includes four primary clusters of questions that relate to types of analytics used here:

• Descriptive Analytics — looks at answering “what happened?”, by analyzing and interpreting historical data to describe what happened in the business.
• Diagnostic Analytics — in answering the question of “why did it happen?”, this step involves looking at the correlation between historical data and primary data & metrics to explain the reasons behind behavior observed in Descriptive Analytics.
• Predictive Analytics — the focus here is on answering “what will happen?”, where the data can be used to forecast future data and behaviour based on the historic understanding and context. Various approaches to algorithms and models can be utilized, and the volume of data determines the specificity and accuracy of algorithms and results predictions.
• Prescriptive Analytics — seeks to answer “how can we make it happen?”, where the best course of action and actionable insights are based on Descriptive and Predictive Analytics.

Interpret Results

After analyzing the data and possibly conducting further research, the next step is to interpret the results. Data experts do not ever look to prove that a given research hypothesis is true, but rather that the analysis has failed to reject the hypothesis.

Data experts will typically ask the following or similar questions while interpreting the results:

• Does the data address the problem? Does it highlight new or existing business opportunities? How and where?
• Does the data help to defend against any objections? How?
• Are there any limitations to the conclusions, any angles that were not considered?

If in the end, the interpretation of the results holds up under the above questions and considerations, then it is fair to assume that the analysis has exposed critical opportunities on which the business can act.

It is important to note that we may only reach this point after numerous iterations of this stage of the analysis. It is very likely that it will need to be repeated before we are able to answer the questions being asked. Only then can we proceed to using the findings to inform and improve an organization’s internal and external processes.

Data analytics companies understand the value of a rock-solid data analysis process. With the right data, organizations are able to make better, data-driven decisions on how they run their business, the products and services they offer, and how they engage with customers.

There’s never been a better time to make sure that you’re getting the most out of data — let us help you get started with asking the right questions and making more informed, data-backed decisions. Contact us today.