So, you are looking to start an Analytics Program within your organisation. You have the tools ready, you have the resources designated for the task, and you have all the required process you need in place to run a robust Analytics program. You expect to see a massive revenue growth within a year; however, after the passage of 365 days, the outcomes are well short of your expectations. At this point, you have a set of questions to ask yourself:
- Where could I have gone wrong?
- My organisation has a massive volume of data. Was the quality of my data not up to mark?
- I had all the tools in place, with Artificial Intelligence automating all the processes. Where did I fall short?
- I have received data from multiple sources? Is this causing the shortfall?
- Have I ensured that the data residing in my data lakes are free from breaches and attacks?
All these questions which arise could ultimately lead you to lose faith in your analytics program.
Over the past several years, we have seen how data analytics has evolved from the simple exploratory level to the current predictive level. With Business Intelligence (BI) playing the pivotal role in decision making, organizations are seeking the power of advanced analytics and machine intelligence to attain agility and competitive differentiation. Telcos, which own the most significant share of customer data among all the industries, are in the best position to leverage them to achieve higher levels of maturity. However, a recent KPMG report reveals a paradox that despite the huge investments in data analytics, organizations are not able to build value around it due to lack of trust. According to the report, only 35% of decision-makers have a high level of trust in their own organization’s analytics, and 25% admit that they either have limited trust or active distrust in their analytics. Moreover, only 10% said they excel in managing the quality of data and analytics, and 13% said they excel in the privacy and ethical use of data and analytics
What Causes the Trust Gap?
The above findings come as no surprise considering the growing complexity associated with handling the data originating from disparate sources. Many studies now indicate that the once 4 Vs to describe key aspects of data (Volume, Velocity, Variety, and Velocity) has now grown to 10 (to include Variability, Veracity, Validity, Vulnerability, Volatility, and Visualization).
But besides the growing complexity of data, there are multiple other aspects which are leading to the trust gap, some of which are captured below.
Quality- a top concern
As the data grows more complex, analysis can be challenging. Poor data quality or incompetent analysis can lead to disaster. As Gartner puts it, “As organizations accelerate their digital business efforts, poor data quality is a major contributor to a crisis in information trust and business value, negatively impacting financial performance.”
Working with false or incomplete data could result in uninformed and biased decisions, which could prove harmful to the overall business. Gartner has also estimated that poor data quality can lead to an average of $15 million per year in losses.
Can we trust machines?
In today’s machine-controlled analytics landscape, building trust becomes even more challenging. The advent of artificial intelligence (AI), coupled with the advancements in machine learning (ML), has opened a plethora of opportunities in data analytics. We have seen many horror stories wherein placing complete faith in AI without human intervention has had disastrous consequences.
Integration of disparate data
Considering that organisations do not have a single source of truth when it comes to the data they gather, data integration is another major roadblock, resulting in poor execution and sometimes complete failure of analytics implementation. Organizations which are slow in their transformation journey confront challenges in integrating data of different formats. They lack the skills, training, and tools to build a centralized access and control policy. Historically, the self-service concept is appealing but has often fallen short of expectations due to barriers faced at multiple levels – technology, people, and process.
Security-an everlasting concern
There is no respite from data breaches and misuse. The fact that fraudsters are making headway by exploiting advanced techniques escalates the concerns. The thin line between the security breach and the reputation of an organization brings the transformation to a halt.
These are but a few reasons to why a trust gap is being created, but they are very real. Should it remain, the trust gap will lead to severe implications in terms of competitive advantage, operational efficiency, and growth. The inability to rise as a data-driven organization means that they also lag mature organizations considering data-driven organisations witness 23x Customer Acquisition, 6x Customer Retention, and 19x Better Profitability (according to McKinsey). And non-data driven companies miss out on all these benefits.
It is clear – The time has now come to bridge the trust gap! The only question that now remains is, how? Stay tuned to our blog for the answer.