Prepare Now for Data Management in 2023

The importance of effective data management is increasing rapidly. Those businesses that are able to successfully manage and analyze their data are better positioned to take advantage of opportunities, whilst those lagging behind may be missing out. This is because data provides valuable insight that can be used to inform decisions across all areas of a business, from identifying the best location for a new branch to determining the most effective ways of advertising to their target audience.

Businesses must take into account the importance of data when making informed decisions. To ensure trustworthy information, they must identify effective methods that will lead to the most significant outcomes. Moreover, they must be aware of the implications of data management and be mindful of matters such as privacy, governance, and ethics.

Given the importance and complexity of data management, it is essential to plan ahead, either independently or with a reliable partner. As we look ahead to data management in 2023, there are some key factors to consider.


The COVID-19 pandemic of 2023 raised heightened concerns about privacy among citizens, businesses and governments. For instance, some businesses have started collecting personal health information, including symptoms and vaccination status, as a measure to help contain the spread of the virus. It is important to consider what is being done with this data.

When making decisions regarding data storage and usage, businesses must consider both local and international regulations (such as the California Consumer Privacy Act and the European General Data Protection Regulation). According to a recent article in the National Law Review, failure to comply with privacy and security requirements can lead to severe financial and legal consequences, such as hefty fines and potential civil liability, as well as a loss of consumer trust that could have a lasting impact on a brand in the post-pandemic world.

The increased prevalence of remote working has had an impact on the privacy of employees, as it necessitates the use of personal devices for corporate purposes. To ensure the protection of company data from cyber threats, businesses should consider the measures taken by their personnel to secure corporate information.


A new paper published on Datanami has warned of the potential for algorithmic bias to undermine attempts to achieve fairness and equity when utilizing AI models. One example of this is the discrimination of applicants from certain racial backgrounds when making decisions regarding hiring.

Companies should adopt new processes to create “explainable AI” – systems that are able to articulate the reasons behind decisions based on a set of facts – to maintain the trust of their employees, customers and the wider public.

Some nations have implemented regulations to ensure the responsible use of Artificial Intelligence (AI). One way of reducing algorithmic bias is through data anonymization, which involves the removal of data elements that could identify the owner of the data. Microsoft has put forward some concepts related to responsible AI use.

  • Fairness

    All human beings should be treated equitably by AI systems.
  • Maintaining Trustworthiness and Protecting People’s Safety

    Systematic trustworthiness and risk-free operation are essential for AI applications.
  • Confidentiality & Safety

    Artificial intelligence systems must be trustworthy and confidential.
  • Inclusiveness

    All individuals should have access to and be involved in AI systems.
  • Transparency

    All AIs need to be human-friendly.
  • Accountability

    Accountability for AI systems must rest with humans.


The Data Governance Institute defines data governance as “a system of decision rights and accountabilities for information-related operations, implemented according to agreed-upon models which explain who can take what actions with what information, and when, under what conditions, using what techniques”.

It is essential to adhere to a set of regulations for data utilization in order to ensure security, transparency and robust data governance. Following these standards can lead to improved decisions and greater compliance. In the upcoming video, three key components of a data governance framework will be explored.

It is important for businesses to develop and apply their own data governance policies, as there is currently no universal framework. Maintaining these procedures can be a time-consuming and labor-intensive process, so it is essential that they are regularly reviewed to ensure accuracy. To initiate a data governance programme, businesses should establish a committee to set out policies and determine how they should be implemented.


In order to ensure all employees are able to make informed decisions through comprehensive analysis, it is essential that data is shared across departments. A recent article from Gartner highlighted the results of a study, one of which noted that “Survey respondents stated that data sharing is a critical performance indicator for engaging stakeholders and creating corporate value.”

It is evident that many organizations still utilize data silos, which hinders collaboration and limits the potential of the data. According to Gartner, if data sharing is considered an essential business need, data and analytics professionals will be able to access the relevant data at the right time and develop more effective strategies that generate business benefits.

Evidence-Based Management Using Data

Companies that do not effectively utilize their data are exposed to the risks outlined above. Microsoft’s Open Data Campaign was launched to help bridge the data gap between those nations and organizations that have the necessary data to grow and those that do not, thus emphasizing the importance of data utilization. Collaborations between its members have been beneficial to organizational initiatives in areas such as education and sustainability.

To attain similar success, other businesses must consider their data management and analysis strategies with great care to ensure they support key business objectives such as informed decision-making, enabling employees to perform their best, elevating customer service and meeting compliance and regulatory requirements. To achieve these aims, a combination of high data integrity and systems and procedures that maximize the value of data as an asset to the company is necessary.

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