Advanced Analytics: 4 Simple Steps For Enterprise Adoption

86 Percent of Predictive Analytics Users Report Tangible Gains to Their Bottom Line – Forbes Technology Council

“Data is the new Oil “ is a quote we often hear these days and regardless of the industry, it holds true for every organization. Currently organizations are capturing data from both internal LOB systems like ERP, CRM, HR systems and external sources like Market Research, Syndicated data, Social feeds. etc.

As more and more organizations show enthusiasm to adopt emerging technologies like the Internet of Things, the amount of organizational data is evidently set to increase. Based on my interactions with various customers and senior executives there are only a few organizations who have unlocked the value of this data.

The rest are still unsure on how to unlock the value and become analytical competitors in their industry. The recent progress and advancements made in Machine Learning and AI along with success stories reported in media have put enormous pressure on senior Business and IT leaders to begin using these modern technologies to unleash the value of analytics and make data work.

While many organizations have already succeeded at leveraging  descriptive or operational analytics and have made significant progress in their journey towards diagnostic analytics, a majority of them are unsure of taking the next step – introducing predictive and prescriptive analytics.

Here are four steps to implement and drive adoption of advanced analytics in your organization.

1) Create an analytics Centre of Excellence

Creating a robust Analytics CoE ensures the effective delivery of these analytics throughout the organization. Ideally the CoE should consist of a cross-functional team of both business and IT leaders to ensure everyone is aligned to organizational objectives. Some organizations have also established a separate “Chief Data Officer” role.

These practices ensure the primary focus remains on creating the culture of analytics and also ease the burden on the CIO who can solely focus on data security and infrastructure sustainability.

2) Set objectives and recognize potential use cases

Once the CoE is set up, the concerned stakeholders need to set objectives and recognize key business areas in which these advanced analytics can be used.

The primary objectives for using any type of analytics are to

  • Enhance operational efficiency and reduce costs.
  • Increase revenue.

The implementation strategy of advanced analytics also needs to be aligned with these two key objectives to extract the maximum value. IT and Business leaders need to recognize potential use cases which are inline with these objectives.

For instance, using these analytics:

  • A CPG company can detect the right marketing and sales promotions and campaigns.
  • A logistic firm can improve the safety of  drivers, ensure fewer accidents and thereby reduce the total insurance payout.
  • Firms which operate with heavy machinery can enhance operational efficiency.

While these are only a few, the use cases vary based on the industry.

CPG companies can use advanced analytics to predict sales

3) Start a Pilot   

After identifying a use case, instead of going all out during implementation, a pilot program needs to be started to get a gist of the final impact and measure the results. The different tools that can be used for the pilot program include:

  • R: An Open source statistical  tool with various commercial flavors like Microsoft R Services with added capabilities.
  • Python: Another open source language which is used by a lot of data scientists.
  • Machine learning platforms like Microsoft Azure Machine Learning and Google Tensorflow support.

Algorithms can be used for regression, classification, clustering etc. The final outcome needs to be analyzed and the involved team needs to measure the actual impact created by these analytics in the scenario. Based on the analysis, the team should decide whether to roll out the project for the entire organization or start another pilot for a different use case.

4) Drive User-Adoption With Chatbots

80% of businesses want chatbots by 2020 – Oracle.

The success of any analytics project depends on users’ willingness to adopt it. In most organizations employees need to go through numerous excel sheets, PDFs or depend on the MIS department. This procedure is difficult to incorporate in everyday workflows and demotivates users to use data during decision-making.

This tedious process can be eliminated by deploying AI-Powered chatbots. Bots are built using AI-powered chatbot platforms and frameworks like Microsoft Bot Framework. They extract data from the existing BI systems and send it to the relevant user via chat. The conversational QnA interface of chatbots lets employees ask one question at a time and obtain key metrics, updates, trends etc.

Since bots can be deployed on any actively used organizational messaging platform like Skype, Skype for Business, Slack etc. users don’t have to login and switch multiple BI systems (which is the case in some companies due to mergers and acquisitions)  for accessing information.

They can simply ask natural language questions like “what is my predicted sales for next quarter “ or “which promotion is likely to give me best results in this store “ or “what is the expected revenue from all stores in this region “ to a virtual chat assistant to get answers.

Not just BI platforms, once integrated these assistants can also be used query data from LOB systems as well. For instance, these bots:

  • Allow data entry into CRM systems
  • Get a case file in a legal industry scenario
  • Provide marketing related documents for a CPG sales person

They act as a single window solution for accessing both BI and process information.

Here’s an example of our Sales Intelligent Assistant delivering insights to teams.

Wrapping Up

Creating a culture of analytics and data-driven decision-making through out the organizations has become inevitable for businesses to stay relevant. Companies already are drifting from or expressing keen enthusiasm to move from traditional analytics to advanced analytics. Acuvate provides AI-powered Business Intelligence offerings which include predictive analytics and BI chatbots. We hold deep expertise in providing custom BI solutions and strategy setting for CPG companies.

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