Analytics maturity is critical in an era of automation and intelligence

Though digital businesses are aware of the significance of data analytics, many organizations are still grappling to stay afloat in the oceans of data. Many others are yet to experience the advantages of being completely empowered by data analytics.

Naveen Arigapudi Aug 24th 2018 A-A+
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In order to thrive in the digital era, enterprises are enthusiastically integrating advanced analytics and business intelligence (BI) to deliver flawless customer-experience. Though digital businesses are already aware of the significance of data analytics, many organizations across sectors are still grappling to stay afloat in the oceans of data; while others are yet to experience the advantages of being completely empowered by data analytics.

There are four models of maturity based on the level of automation of the analytics process—

Descriptive Analytics focuses on the past and the present to determine what happened and what is currently happening.

Diagnostic Analytics finds out why something happened or is happening.

Predictive Analytics makes predictions about future events such as ‘what will happen?’ It is instrumental in identifying frauds, enhancing operations, and reducing risks.

Prescriptive Analytics finds out the effective actions you should take for a given situation and how you can implement the actions.  This form of advanced analytics can provide you with more perceptive answers pertaining to your most popular products or your target-customers. Prescriptive Analytics is interrelated with descriptive analytics (that explains what has happened) as well as predictive analytics (that predicts what will happen in future).

Currently, a lot of organisations are stuck in the descriptive stage, thus, making use of the conventional methodology of business intelligence. They are still unaware of how predictive analytics can enhance their business competence.

Analytics Mature organizations versus Analytics Immature organizations

Analytics Mature enterprises fortify their analytics capabilities by investing in quality data (consisting of people data, performance data, and program data), high-tech tools, data-savvy workforce (comprising data scientists, technical specialists, business analysts), and processes that support the analytical management. All these factors are mandatory for an organization to build an analytics machine of high performance. However, a lot of analytically immature organizations do not invest in any of these factors while there are some that do extremely well only in one or two of the above-mentioned areas.

Analytics Mature organizations also create a tactical strategy for accumulating and categorizing data for creating business value. However, analytically immature organizations lack the capability of generating deep, data-driven insights as they do not have the systems and processes necessary for capturing the required data or they end up accumulating ineffectual data.  Moreover, such organizations do not deploy the right technology for hoarding and accessing data. Last but not least, analytics mature organizations can take risks more comfortably compared to analytically immature businesses.

Analytics Maturity is the building block for digital businesses

Analytics Maturity is the keystone of AI-led businesses. Analytics maturity model makes your work simpler by comprehending the present state of affairs so that you can set your analytics goals and focus on creating more value for your data.

For example, let us take the example of the sales department of an analytically immature organization—

There are no defined sales methodologies to follow and no definite way of identifying what delivers fruitful results and what does not. New sales representatives are generally allotted a random quota and time-to-full-productivity metric is not taken into account. Such organizations have a low level of accountability as sales people have a tendency to forestall the best sales practices.

The responsibility of sales management lies solely on top sales performers who may not be suitable for the task and may have individual sales goals to meet. Due to lack of reliable data, such organizations also do not utilize sales analytics. It goes without saying that such a structure generates erratic predictions and unreliable results, often ensuing missed sales targets and disastrous on-boarding programs.

On the other hand, the sales department of an analytically mature company is characterized by an unbiased corporate culture that deploys the best sales practices. Their goals and sales targets are precisely defined and their resources, priorities and activities are in harmony with each other.  They emphasize on technological infrastructure and have a well-defined sales structure in place so that new sales hires can easily comprehend what they have to deliver.

Analytically mature companies implement regular coaching and make use of sales analytics to reach their target customers in a better way. Sales analytics are reviewed regularly to comprehend how and where the marketing resources should be utilized. Since the predictions are precise, results can be forecasted with a high level of accuracy and deals having a low probability are immediately eliminated from the sales pipeline.

Challenges enterprises face in becoming a more mature organization

  • Resistance to change is a major challenge in becoming a more mature organization. Underlying fears and apprehensions caused by improbabilities of change trigger the feelings of resistance. While resistance to change is a common human behavior, it can be avoided by applying effective change management.
  • Getting hold of the data required for developing models is another obstacle.
  • Another challenge is the difficulty in the deployment of models into the organization’s operational systems along with its products and services. Moreover, some organizations find it difficult to combine various models for meeting the overall requirement of an organization.
  • It often gets difficult to calculate the business value engendered by analytics models. Moreover, the analytics models deployed by the organization fail to generate the anticipated business value at times.

Steps to move up the analytics maturity model

Since predictive analytics highlights the future events, in order to pull off competitive advantage, you must look for predictive answers to solve your organization’s critical problems. You should take the following measures to shift from descriptive analytics to prescriptive analytics:

  • Improve stakeholder engagement: The key stakeholders should be aware of the organization’s business goals and must be in accord with organizational liability. Vaguely-defined business goals are difficult to understand and it eventually leads to failure in business as well as your predictive analytics initiatives.
  • Estimate resources: To implement predictive analytics, you need to conduct a scrupulous assessment of the available data along with the IT resources and the vital resources mandatory for running your business. While assessing the resources, ensure you have sufficient internal and external data resources.
  • Operational planning: Finally, you need to make a solid operational plan and demonstrate your analytics model by documenting the end result of predictive testing in a dashboard or report. Analytics reports are crucial as they help you make tactical business decisions with insight. Additionally, you can reap the benefits of predictive analytics by implementing these reports in your organization’s operational set-up

Prescriptive analytics is rather a new domain in data science, but its adoption is gradually soaring in the market to help businesses identify the shifting trends and customer behavior more accurately. If you want to transform your big data into practical insights, predictive analytics model has the potential to revolutionize your AI-driven business.

 

Naveen Arigapudi is AVP – BI and Analytics, Infogain.
 

Disclaimer: This article is published as part of the IDG Contributor Network. The views expressed in this article are solely those of the contributing authors and not of IDG Media and its editor(s).