4 Pillars Of Trusted Data Analytics

It’s time we should discuss trust in data analytics


Instinctive and subjective decision-making is being replaced by objective, data-driven insights. It is empowering companies to better serve their customers, drive operational efficiencies and mitigate risks. What is more enticing and frightening is that analytics now has its reach far beyond organizational boundaries. Considering the power it holds, trust in data analytics has become a non-negotiable priority for businesses, individuals, and the society.

With so much exposure to data, analytics, and the kind of outputs expected from it, there are questions mushrooming about the trust that is placed in it, and the newer ways of decision making. Here are the four reasons why we should discuss trust in data analytics:

1. Analytics is vital to business decisions, more than ever

Data and analytics have taken the center stage when it comes to business decisions, especially for making profitable growth by understanding customers and creating new customer experiences, streamlining operations, improve overall productivity, and mitigating risks. More and more organizations are adopting one or the other types of analytics, from traditional BI to real-time analytics and machine learning. Some of them opt for predictive analytics and some go for advanced visualization, in addition to traditional static charts and graphic presentations.

2. Lives depend on data analytics

Analytics has succeeded in influencing behaviours and drive decisions even at individual levels. Algorithms that support critical decision making across industries like healthcare, insurance, banking, fraud detection, autonomous vehicles, national infrastructure, security, etc., are known to have lifelong consequences for individuals. This is why businesses and consumers trust algorithms that make decisions on their behalf.

This is not only for high-risk businesses. Even low-risk business applications, customers, and executives trust their data analytics. Organizations targeting consumers based on inaccurate predictions may face situations where consumer trust is battered, and executives who depend on those predictions lose confidence in making informed decisions.

3. Algorithms are integral and can’t be pulled apart

Algorithms are not some of those physical machines that can be switched on or off, at will. Internal working of algorithms and models is largely hidden, or one can say, is way too mysterious for a business owner to get hold of. They usually find algorithms to be too opaque to be verified.

With the increasing benefits that data and analytics offer, its popularity has increased manifold. More and more players and third-party data analytics solution providers have started playing vital roles in the entire analytics value chain. However, the lack of transparency has created suspicion which has made people sceptical about how it has impacted society.

4. Reputational risk associated with data analytics

With so many organizations shifting their decision-making to algorithms and hidden analytics, the reputational risks are witnessing a completely new degree of severity. Customers, investors, and regulators will not protect data breaches, miss-selling of products, and services if they do not believe that data and analytics can make value addition. Also, organizations have a tendency to link their reputation to the use of analytics, and they are not wrong completely either. They believe that by using data analytics, they are inevitably exposing themselves to reputational risk.

Why companies lack confidence in integrating data analytics into their business processes?

Most people have similar instincts when it comes to the importance of 'trusted data and analytics', as a business owner and as an individual.

• They want to know that the data used and outputs derived are correct.
• They want to make sure their data is used in a way they understand.
• They want to make sure their data is used by the people they trust.
• They want to make sure their data is used for a purpose they approve of.
• And they want to know if something is going wrong.

However, none of these facts is clear nor are there any assurances of authenticity. This is because trust in data analytics is like trust in products and people. It is usually driven by a fine blend of 'perceived trustworthiness' and “evidence of its actual trustworthiness' - and none of them is easily accessible.

What everyone tends to forget, intentionally or unintentionally, is that trust is ultimately driven by actual trustworthiness and completely based on performance and impact. Trusted data analytics is not a vague concept, as rigorous strategies and processes for improving data quality and protecting data privacy are followed to maximize trust. Aspects of ethics and integrity may take some time to surface. Organizations today should opt for a systematic approach to trust, which spans across the lifecycle of analytics. Enlisted are the pillars of trust:

Which are the four pillars of trusted data analytics?

1. Quality of analytics

• Are fundamental building blocks of data analytics good to go ahead with?
• Is the organization mature enough to understand the role of data quality while taking up data analytics initiative?

2. Effectiveness of analytics

• Is the analytics initiative working as intended?
• Are organizations able to determine the accuracy and utility of the outputs?

3. Analytics Integrity

• Is data analytics being used in an acceptable way?
• Is the organization well-aligned with regulations and ethical principles; if yes, to what extent?

4. Resilience of analytics initiative

• Does your data analytics initiative optimize the long-term operations?
• Is the organization equipped to ensure good governance and security across the analytics lifecycle?

Each of the pillars of trusted data analytics is relevant and interconnected across the analytics lifecycle, starting with data collection, data preparation and processing, through to data analytics and statistical data modelling, usage and deployment, and, in the end, measuring the effectiveness before going back to the beginning of the cycle again.

Organizations should close data analytics capability gaps

The need of the hour is to come up with ways to establish societal trust in how organizations operate in the emerging data-driven society. We are moving into the world of data, more quickly than we can even imagine. Our behaviour and decisions are wedged by systems heavily fueled with data. Looking for a simple and straightforward solution for trusted analytics would be sheer foolishness. Simply waiting to see how things move around in the world is something no organization can afford. Despite different levels of investment, more sophisticated D&A tools do very little to enhance trust across the analytics lifecycle. The trust gap cannot be closed by simply investing in better technology.

Looking small

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