Every function within an organization generates a lake full of data. It’s now up to organizations to decide whether this data will flow freely, irrigate critical decisions made by the organization, or just stagnantly sit idle.
Most organizations are still dependent on experience-based decision-making, with executives having decades of functional & domain expertise. However, experience shouldn’t be the only driving factor for taking strategic decisions. In the era of continuously upgrading technology, data analytics empowers organizations to graduate in data-driven, well-informed decision-making processes.
However, at many organizations it is proving difficult to build trust, regarding data.
1. Build confidence around Data
The first step to build trust regarding data is to build confidence in data processes. It’s important to know if the data is consistent, well sorted, arranged and presented, accurate? Is the data explained well with a story? To ensure your data conveys the right message, it should be self-explanatorily. Otherwise, people may start assuming different things that wouldn’t serve the purpose of data analytics.
Once you start building the right stories around your data, you will feel more confident when presenting and sharing it with your colleagues. If your colleagues are aligned with the data, then establishing confidence among different cross-functional departments becomes easier. Without stakeholder participation, any data project within the organization might fail.
2. Aligning teams for Data Accuracy
Data analytics shouldn’t be based on a sample or a subset of data. Getting accurate reporting is a team effort and not an individual responsibility. Therefore, organizations must incentivize team members who report data on a consistent basis.
One major challenge organizations face is having incomplete or inaccurate data due to a lack of consistency. Issuing warnings on data shaming isn’t going to help. The key to data accuracy is aligning teams, showing them the bigger picture about your data project, and motivate them do it voluntarily.
3. Fine-Tuning your Big Data Technology
Any Artificial Intelligence technology needs large amounts of data for improving accuracy. Many AI projects fail due to the use of sample or subset data, which is either too small amount of an amount of data to be beneficial or data that is irrelevant. On the other hand, in some platforms, too much data can also lead to delays in outcomes, reducing the ability to take proactive actions. Hence, it is extremely necessary to select the right technology partner for your big data project.
The approach for any big data analytics project shouldn’t be myopic or just to get past existing challenges. Organizations must continuously study and fine tune their technology in order to get desired outcomes.
4. Be Transparent for
Organizations shouldn’t be resistant to accept the outcome of the project. Sometimes, the findings can be hard to digest, such as negative customer feedback, or any other negative publicity.
One example of this is the perception of bias in decision-making. In late 2019, a large bank experienced public backlash when it approved two different levels of credit for a married couple who claimed to have identical assets and credit scores. On social media, the accusations of gender bias went viral. The bank strongly disputed the accusation, but due to the inability to prove it, perception became reality. Eighteen months later, after a lengthy investigation by regulators, the bank was cleared of gender bias in its algorithm but was scolded for lack of transparency, undermining consumer trust.
The ability to replicate, demonstrate, and articulate data is at the core of building trust for data analytics. If your data is easily understandable and tells a meaningful story, it will subsequently build trust and confidence across organizations.
QEval is proud to deliver a wide range of data-driven solutions to companies wanting to improve their company experiences. We provide a wide array of data analytics solutions, that combine human intelligence with artificial intelligence, to create comprehensive resolutions for customer experience within the corporate space. Contact us for more information on any of our customer experience monitoring solutions.
