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​7 Ways To Strengthen The Pillars Of Trusted Data Analytics

Looking at how companies can increase their trust in data analytics

20Dec

Data analytics (D&A) is all about using data to drive business strategy and performance. It includes a wide range of approaches and solutions; looking backward to evaluate what happened in the past and looking forward to planning and predictive modeling. Data analytics examines large amounts of data to uncover hidden patterns, correlations and other insights for enterprises. We can say that data is at the heart of everything that any company does, and whilst this is true, trust is an element that cannot be discounted. So let's see how to strengthen the pillars of trusted data analytics.

Charity begins at home, and so organizations should start by creating a solid foundation of trust in their data analytics. For organizations, trust can never be a project, and strengthening the pillars of trust can never be a one-time exercise or a compliance tick-box. It has to be a continuous endeavor, spanning the company and its functions. It should start from data collection to data processing, to outcomes and measurement of value. There are no roadmaps for driving and instilling trust and no perfect answers. Building trust in analytics needs every employee of the organization to look at their D&A lifecycle.

7 best practices for companies to strengthen the pillars of trusted data analytics

1. Identify trust gaps

Enterprises should start by finding organizational areas where trusted analytics is most critical to the business. Then they should find out its weaknesses. The focus should then be to start working on improving the weaknesses within all four pillars of trust. Straightforward changes like using simple checklists often reduce key risks.

2. Clarify & align goals

Companies should decide the purpose of data collection and the associated analytics. They can make the D&A performance and impact measurable. The aims and incentives of the data analytics owners should both be aligned with the goals of the end users and also with those who are affected by it. Remember, misalignment or lack of clarity around the purpose of data analytics goals are sufficient enough to create mistrust. It will dilute the ROI and invite inadvertent misuse of data.

3. Increase awareness & engagement


To break the cycle of mistrust, it is necessary that enterprises invest time and efforts to bring awareness and understanding of data and analytics among it business users. Streamlining the processes will involve key stakeholders and establish multidisciplinary project teams comprising of data and analytics leaders, IT/business stakeholders across different departments in order bring substantial change.

4. D&A culture and capabilities – foremost guardians of trust

Data and analytics professionals, right from data entry operators to data scientists, are all critical to uplift the wider understanding of data and analytics across the organization. Identify the gaps and areas of improvement in your organizational capabilities, governance, structure, and processes. And don’t forget to have the best data analytics experts by your side, with quality assurance of experimental designs, A|B testing and, other means of validation. Make trust in data and analytics one of the core values of the company.

5. Use a second set of eyes for transparency, maybe even a third

Actions that help an enterprise improve their data and analytics transparency include establishing cross-functional teams, third-party assurance, peer reviews, encouraging whistleblowers and strengthening QA processes as valuable ‘guardians’ of trust. Reviewing every data and analytics challenge independently – will make a difference.

6. Take a 360-degree approach

To drive trust in analytics within the organization, you as a company should look beyond the traditional boundaries of systems, organizational silos, and business cases. Enterprises took the portfolio approach to look at the value and risks that data and analytics brought to the organization as a whole. Then, they created 'meta-models' and cross-functional teams to identify, assess and control the dependencies between these models to help these companies.

7. Create a model for D&A innovation

Empower your data and analytics teams to push the boundaries of innovation without the fear of failure. Building a data innovation lab that allows data scientists and all involved in the business to promptly test new ideas, will definitely have a positive impact. Consider ROI beyond specific performance objectives of your data analytics project. Incentivize employees for innovation, they will be appreciative of it and it will motivate them to make more innovations.

Conclusion

Data and Analytics hold the power to unlock untold value. But for that to happen, companies you need to trust what it has to say. Though a majority of enterprises say that insight derived from data and analytics are critical to their business decision making, only 1 out of 3 companies trust the analytics they generate from their operational data. Aforesaid are some of the best practices that leading organizations are following to improve the trust they have in their data and analytics. Organizations capable of overcoming the trust gap quickly will be the ones that will be better-placed to make faster decisions more accurately and with much greater confidence. Those will be the companies that will win in the future.

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