How to Become a Data-Driven Enterprise

What does it take to become a Data-Driven Enterprise?

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What is Data & Information?

Data can be termed as unstructured, unanalyzed, and uninterpreted raw facts that inherently have no significance or purpose. Data, as described, can be very useless in its original format but can be transformed into a potent force for organizations when structured and interpreted.

Purpose of Data & Information

Conventionally organizations would describe their assets as money, property, equipment, inventory, and human resources. Now many organizations are attempting to leverage data to drive business operations and decisions. However, it is surprising to find out that:

Only 17 percent of organizations say they are very or extremely effective at maximizing the value from the data they hold.

— Harvey Nash/KPMG CIO Survey 2019

Even with the wellspring of data and information, companies are still yet to leverage and harness data to completely extract all the valuable insights the data provides. In this modern and competitive environment, company’s are expected to leverage information because all other resources like human resources, material resources, and financial resources can only be harnessed completely through the support of information to help managers make more informed decisions.

Decision-making relies heavily on analytics now and the best companies can extract and interpret their data to make decisions. Companies that do not follow suit will end up falling behind these other successful companies.

Characteristics of Good Data & Information

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A challenge many organizations are facing is being able to acquire “Good Data”. There’s a bit of subjectivity revolving around what good data is as not everyone requires the same data. However, we can begin by evaluating data by these characteristics and judging their importance based on the organization’s long-term objectives which will in turn aid in the development of short-term objectives.

These characteristics include:

• Relevance & Timeliness: There should always be a reason or purpose for data collection to justify the resources being allocated to acquire the relevant data to the end-user. Additionally, the data should be collected at the right moment to enable the end-user to have reasonable confidence in their decision.
• Accuracy: The data needs to be sufficiently accurate for relevant users. Organizations are to perpetually increase the accuracy of data.
• Completeness: There is a need to ensure that the records are full and contain enough information to draw insights. Finding null values is a necessary step in ensuring the completeness of the data. This is necessary to curate high-quality data sets.
• Consistency: Consistency involves two values from separate data sets matching or aligning.
• Validity: Validity involves checking the format of the data and the data types.

Common Data Challenges

There are several challenges organizations face when acquiring and extracting insights from data to make informed decisions. These challenges include:

• Lack of Data Professionals

Considering the large amounts of data generated every day, modern technologies adapted to handle such large volumes of data are required. Companies require the likes of data analysts, data scientists, data engineers to operate these tools and derive insights from enormous data sets. This is mostly because the tools are evolving at such an exponential pace but the professionals are not able to keep up with the pace.

• Timeliness of Data Acquisition

Managers need data at the right moment and as early as possible or sometimes even in real-time. However, most organizations are not able to acquire the data that quickly and this brings about challenges for management. Organizations need a strong data strategy in place to facilitate the acquisition down to the communication of data to relevant stakeholders.

• Securing Data

Ensuring the security of massive data sets has proven to be a challenge for most organizations. Companies already have a hard time acquiring, storing, understanding, and analyzing their data and do not even pause to consider the security of their data until later. Over time these data, storages become hotbeds for users with malicious intentions. The average data breach costs in 2021 were 4.24 million.

Data As A Corporate Asset

Businesses need to manage their data to maximize its benefits. Company data needs to be well managed to support and facilitate decision-making by management. If this is not done, terrible business decision-making and a number of compliance issues can arise.

The solution to avoiding terrible decision-making and compliance issues is having ensuring Data Governance and initiating Data Management.

Data Governance

Data governance involves the assembling policies, standards, processes, and metrics that enable organizations to utilize data effectively and efficiently ensuring they meet their business targets. Moreover, this develops responsibilities and processes that bolster security and quality across a business.

Data governance helps answer questions like:

• Who can access what data?
• Who has ownership of the data?
• How much of our data is compliant with new regulations?
• What security measures are in place to protect data and privacy?
• Which data sources are approved to use?

Data Management

Data Management performs the procedures and policies to support and facilitate decision-making for management. It essentially describes collecting, assembling, securing, and storing company data for further analysis to aid business decision-making. Due to the exponential rate at which data is being generated and consumed by organizations, management solutions are necessary for making sense of such large volumes of data.

What is a Data-Driven Organization?

A data-driven organization is an organization that makes informed decisions based on data. The core of a data-driven organization revolves around the internal culture of the business and the way it perceives information and acts upon it. This sort of culture has to reflect from the top to the bottom of the organization.

A major issue preventing organizations to be data-driven stems from the opinion of the highest-paid person in the organization. These hindrances tend to approach decision-making traditionally and will veto decisions even when most of the data points in the direction they have rejected.

How to become a data-driven organization?

There are roughly six steps to becoming a data-driven organization. These include:

• Understanding The Business

To start with, there needs to be some understanding of the business to identify what data is relevant to be collected and how it should be managed.

• Align People, Processes, and Technology

Moreover, the people as mentioned earlier need to be instilled with the data culture from top to bottom in the organization. The technology also needs to complement the people in the organization as the data management can only work effectively as the skills of the professionals in the organization.

• Knowing where the data lives

It is necessary for organizations to be aware of where they can acquire relevant data to make their decisions.

• Connect, relate and transform data

Data professionals in the organizations should be able to easily access data to begin analysis.

• Gain insight from the data

The data professionals should be able to derive meaningful insights from the data.

• Take action

In the end, organizations should be able to take action on the decisions made and have performance metrics to measure performance and evaluate them against expected results.

Conclusion

Lastly, a data-driven organization will provide the organization with more confident decision-making, making organizations more proactive and effectively cut costs and increases revenues.