Do you know why implementation of advance analytics fail?

Today every business is talking about data driven approach to business management to leverage the benefits of data analytics and advance analytics in decision making and gaining competitive edge.

The business leaders are investing huge amount in analytical tools, data scientist and data analysts. Still they are not gaining added value in business. Implementation of advance analytics is not proving them scalable solutions.

A McKinsey report shows that only 8% of 1000 respondent are engaged with analytics for scaling of business. Most of top stakeholders are trying to identify the added value by these advance analytical tools. McKinsey has identified in its survey that only a small fraction of value has been unlocked.

Below are some reasons, creating the danger of failure in adoption of analytics

Lack of clear vision

The CEO are investing much in hiring data scientist and adopting analytical techniques but they are lacking in specific vision related to analytics. They are lacking in distinguishing tradition analytics (used for the purpose of reporting) and advance analytics (comprise of predictive and prescriptive tools). The organization are moving towards adoption of advance analytics without having a clear vision in mind that what value this advance analytics is going to add. They are unable to define what specific problem the data scientist is going to resolve.

Undefined analytical roles

Because of lack of clarity in vision, the organization is also lacking in defining the exact roles and responsibility of data scientist and data analyst. Organizations are hiring data scientist without having specific role to be performed and specific issues to be resolved by them. To overcome this issue organization, need to match up the skills and roles of data scientist.

Inadequate analytical capability

Many organization are lacking adequate analytical capability to adopt analytics. With the growing implementation of advance analytics in different use cases the gap between the requirement and availability of data scientist, data engineer is increasing. The available skill sets are not adequate to convert data into valuable insights.

Difficult to measure quantitative value of analytics

The organization has identified many use cases where analytics can add value and they are trying to implement it in different use cases from smaller to the bigger one. But the organization fails to measure the value added by analytics in small use cases in the first year. Without measuring this, companies are investing more and more that is becoming reason od failure.

Also, the companies are not sure about the quantitative impact of these advance analytics and other digital assets on bottom line.

Ignorance of Ethical implications of analytics

Today's businesses are focusing on investing on technology, analytics and other digital assets to become first mover advantage but no one is bothered about its ethical and legal implications. Analytics uses huge amount of data set but no companies are thinking that it may create some moral and ethical issues and lead to violence of regulatory compliance.

These reasons probably preventing effective implantation of advance analytics to add more value in the bottom line. Organization can resolve the above issues by setting clear vision behind adoption of advance analytics, understanding the significant difference between the impact of tradition and advance analytics and develop a visionary sense regarding possible ethical issues and regulatory compliance issues that can occur.

Understanding these issues and taking effort on this will work towards the effective adoption and increased value in business success with advance analytics.

Source: HOB

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