Data management is becoming increasingly important. Companies that manage and analyze data well are better positioned for success while those that lag behind may be missing out on valuable insights. That’s because data can help businesses make the best decisions on everything from where to locate their next office to what kinds of promotional messages resonate best with customers.
But data management is complex. Businesses must start with reliable sources, then collect data efficiently, then analyze it, and draw meaningful conclusions. They must find the right tools to help them get to those conclusions that will be the most impactful. At the same time, companies must take responsibility for the power that data holds. Doing so requires forethought about matters like privacy, governance, and ethics.
Because data management is both so important and complex, it’s critical to plan ahead, either internally or with a trusted partner. Here we present some key areas to consider in looking forward to how you will manage data in 2022.
Privacy was already an issue prior to 2020, but the COVID-19 pandemic forced individuals, companies, and governments to consider it even more. For example, in their effort to curb outbreaks, some companies have been collecting sensitive health data about individuals, such as their symptoms and whether or not they have been vaccinated. What are they doing with this data?
Businesses must consider domestic and international laws (such as the California Consumer Privacy Act or CCPA and the European General Data Protection Regulation or GDPR) when making decisions about data retention and use. A recent National Law Review article states, “Noncompliance with privacy and security requirements can result in harsh monetary and legal penalties, including steep fines and potential civil liability, and can result in a loss of consumer trust potentially impacting a brand into the post-pandemic landscape.”
Another impact on individual privacy resulting from the pandemic is the work from home (WFH) phenomenon, which has forced many employees to use their own devices to perform their work. Businesses must consider the potential for cyberattacks on their employees, as well as the converse — whether and how company data is being kept safe by team members.
According to a recent Datanami article, “Algorithmic bias is a real threat to the goal of achieving fair and equal treatment at the hands of AI models.” This bias can take the form, for example, of treating people of different races differently in the selection of job candidates.
For companies, this situation requires the adoption of new practices to create “explainable AI” — systems that can explain why a decision based on a specific set of data was made — to maintain the trust of employees, customers, and the public at large.
Data anonymization — the process of removing anything from a data set that links it to the owner of that data — is one part of reducing algorithmic bias, with some countries passing legislation to ensure “responsible AI.” Microsoft offers the following responsible AI principles:
- Fairness – AI systems should treat all people fairly.
- Reliability & Safety – AI systems should perform reliably and safely.
- Privacy & Security – AI systems should be secure and respect privacy.
- Inclusiveness – AI systems should empower everyone and engage all people.
- Transparency – AI systems should be understandable.
- Accountability – People should be accountable for AI systems.
The Data Governance Institute defines data governance as “a system of decision rights and accountabilities for information-related processes, executed according to agreed-upon models which describe who can take what actions with what information, and when, under what circumstances, using what methods.”
In other words, it’s rules about how data gets used. These rules are important for maintaining privacy, adhering to ethical guidelines, and generally ensuring proper stewardship of data. A robust data governance program ensures better decision making and improved compliance. The following video describes 3 critical components to consider when setting up a data governance framework:
There are no set data governance rules, so each company must create and implement its own. These processes may take considerable time and effort and must be revisited as needed to ensure they are up to date. Companies that need to start a data governance program should assign a committee to come up with an appropriate set of rules and make recommendations to deploy them.
One of the most important uses of data within a company is sharing it among departments so everyone has access to the best information and the ability to make sound decisions based on effective analysis. In a recent article, Gartner describes the results of a recent survey, including, “Survey respondents reported that data sharing is a business-facing key performance indicator of achieving stakeholder engagement and providing enterprise value.”
Yet, many businesses maintain data silos that don’t allow data sharing. This approach limits the value these companies can derive from their valuable data. Gartner states, “By recasting data sharing as a business necessity, data and analytics leaders will have access to the right data at the right time, enabling more robust…strategies that deliver business benefit.”
Data as a Foundation for Business Success
As stated above, companies that fail to use data effectively risk falling behind. Smart use of data is so important that Microsoft created its Open Data Campaign “to help close the data divide between those countries and companies that have the data they need to innovate and those that do not.” Its activities have included collaborations leading to improved organizational efforts in such areas as education and sustainability.
Companies that want to mimic these successes must plan carefully to ensure their data management and analysis efforts meet critical business goals such as robust decision-making, enabling employees to do their best work, better serving customers, and compliance and regulatory requirements. The key to these efforts is high data integrity combined with systems and processes that ensure data is used to its fullest potential as a powerful business asset.