Data Democratization - The Digital Transformation Accelerator

Digital transformation is a generic term tossed around the IT world to describe almost anything, but what does it really mean? And, how important is data access to enabling digital transformation? Very important. And access to data may be one of the most critical strategic differentiators in the age of AI.

McKinsey & Company defines digital transformation as the rewiring of an organization with the goal of creating value by continuously deploying tech at scale. While effectively adopting and applying modern technology to effect change is at the center of digital transformation, it is not only about the technology. Building a culture that embraces innovative approaches to drive efficiency and better performance is also a big part of digital transformation. Employees’ reluctance to let go of existing and comfortable processes can stall digital transformation initiatives.

Research conducted by Trianz identified ten rules for crossing the digital fault line and successfully implementing a digital transformation strategy. While it is essential to follow all ten rules to transform your organization successfully, data democratization, an approach that enables decision-makers to access data across silos and replace assumptions with data analysis, can help you follow three key rules, which are:

• Break functional silos to be an experience-driven organization.
• Replace assumptions with data analysis.
• Technology does not make successful transformations; people do.

Data Democratization

Data democratization is the process of making data more accessible to more people, no matter their technical skills. This strategy also entails making data more accessible to workers and decision-makers across business or functional silos. Data discoverability is another critical aspect of data democratization. If users don’t know whether data exists, it is not very accessible.

Breaking functional silos with data sharing

As organizations evolve, different teams adopt the tools and processes that work for them and enable them to be the most effective at their jobs, be that a CRM system for sales and marketing teams or ERP systems for manufacturing operations. Consequently, these different businesses and their systems operate as separate silos.

While optimizing vertical business functions has worked well, faster-moving agile practices require organizations to be more aligned and quicker-moving. Successful digital transformation strategies require breaking down these different silos and sharing data.

Decisions based on one department’s data may seem optimal at the group or department level, but these decisions become less optimal without understanding trends in different parts of the organization. When data is shared across business silos, decision-makers can consider the implications of their choices and actions on other groups. Managers can also analyze how trends and decisions across the organization influence events in their operation. For example, investments in thought leadership content can drive new leads and sales, while also potentially encouraging better candidates to apply to job openings. If sales leaders only tracked content investments against leads in their CRM system, the added benefit experienced by HR would go unnoticed.

While this may be a relatively straightforward and obvious example, there are countless synergies and correlations across organizations that, if identified, can drive better organizational performance. The trick is being able to explore data in various systems and uncover that hidden value. This truth is becoming increasingly apparent as powerful AI tools can help us find data that enlightens our understanding of how businesses and markets operate. For example, based on customer service data and past sales, predictive models can foretell a customer’s buying propensity. Aging accounts receivable and slowing sales leads could indicate a softness in the market. The more data that is available, the more accurate these models can be.

Sharing unstructured data

The growing opportunities to mine unstructured data also create more ways to improve decision-making performance. The increasing capabilities of Large Language Models (LLMs) and facial recognition to mine unstructured data make it much easier to analyze. For example, AI can scan emails and tag them as having a specific tone or reference to a particular problem with a product. AI could spot sentiment in a customer email and tag the product mentioned in the communications if a product is not performing as it should be due to a series of events. This metadata can be stored and shared with the manufacturing department, which can compare this data with other data sources to identify the origin of the problem. Suppose there are any additional complaints from other customers. In that case, the analyst can look at shipping data to identify any similarities in the environment where the faulty products were shipped and used. Is there a connection to a manufacturing run, raw materials shipment, or tool change? Identifying complex problems quickly, finding the root cause, and making the appropriate change quickly will separate the winners from the losers in the coming age of data and AI. This capability is only possible if data is shared effectively across the organization.

Share data to create a 360-degree experience

Democratizing data also leads to a more holistic experience for your customer. When data isn’t shared across regions or lines of business, customers can feel like they have fragmented relationships with each entity. Whether they are engaging with the home office through the call center or a branch office in person while on vacation, brands should be able to provide a consistent experience. Without making data available across your channels and regions, sales and service reps don’t know their customers and end up delivering a disjointed experience, degrading the relationship.

Driving Data Culture - Replace Assumptions with Data

Data democratization strategies drive greater data sharing and enable a more data-driven culture. A key piece of surviving in today’s constantly changing and digitized competitive environment is the ability to make decisions with data. Committing to sharing data is vital, but understanding how to make data-driven decisions enabled by data sharing is fundamental to driving your digital transformation strategy forward and remaining relevant.

With decision-makers being able to access more data, they are more likely to use it to support their decision-making. While appropriate training is key to driving data culture and ensuring digital transformation stays on track, training without access to data to exercise these new skills is counterproductive.

Data culture spreads as different groups collaborate and learn new skills and insights. Therefore, it is essential that you also create standards to ensure communication remains consistent as you make data more available. Terminology, calculations, and metrics that differ across domains can lead to miscommunication and errors. Data catalogs and business glossaries that define data sets and business terms can help to support better collaboration.

Empowering People to Drive Digital Transformation

Empowering people with access to data and the skills to use it fuels more innovation and accelerates the migration to a fully digital enterprise.

With widespread training and data access, growth in data culture begins to compound. More access to data and training fuels curiosity, experimentation, and innovation. Professionals learn to find data and use it to answer their questions and dig into trends. They also learn to use data to tell a story and make a case in order to capitalize on a business opportunity. The more people in your organization who follow their curiosity and dig up data to support a trend or opportunity they observe, the more agile your organization will be, and the faster you will get new products to market.

While technology is emerging to enable data democratization, there are still political challenges to overcome. Data is power, and controlling it has political implications. For example, a business group that can make better decisions based on quality data may get more resources than other departments without the same access. A data democratization strategy paired with a federated governance framework can be extremely helpful in bridging the gap between people and their insecurities to support greater collaboration.

Data democratization and federated frameworks also enable people to work together more efficiently. When two groups try to work together or get better aligned, they need to work with the same data sets. If data sharing is already built into an organization's culture, this becomes second nature. With data sharing as a default, understanding data from other groups becomes easier. Terminology, metrics, and calculations may differ, leading to confusion, misalignment, and inefficiencies. Data storytelling and justifying a hypothesis based on uniform data sets makes consensus much easier. With greater innovation and collaboration, organizations can streamline their digital transformation projects.

Use data democratization to break down business silos, drive data culture and empower employees.

Implementing Data Democratization

Data democratization can be a crucial strategy to help you reach your digital transformation goals. Still, as people are empowered to share and use data more freely, certain guardrails must also be incorporated into your strategy. Governance and data quality are core to any successful data democratization strategy.

Governance

When decentralizing access and control, having the right balance of governance and autonomy is challenging. The explosion of “shadow IT” has shown that if central IT dictates what tools technology workers can use, they will look for solutions outside the purview of IT with productivity trumping compliance.

A flexible governance framework that incorporates the needs and requirements of users and IT requirements to ensure data is handled responsibly helps organizations walk this tightrope.

Governance is more than security; it ensures data is accurate, accessible, private, and usable. Quality control is paramount to ensure data is accurate and trustworthy. Without trustworthy data, strategies will collapse as decision-makers lose trust in their data and return to making decisions based solely on their gut and experience.

Well-defined data governance policies and strategies are paramount to the long-term success of implementing data democratization. If you get it wrong, it could set your digital transformation journey back. Done right, you have an entire organization of people who treat data with the respect it deserves and take on the responsibility of ensuring proper governance.

AI in governance

The explosion of AI and its rapid move into mainstream applications is remarkable. The technology is also impacting data governance and supporting data democratization strategies.

Accessibility

AI helps with data access by allowing analysts to access the data they need more quickly. Co-pilot features enable the analysts to simply ask a chatbot to get the data they need, and the AI bot can quickly find and retrieve the data.

Data quality

AI assists in ensuring data quality. Validation models check data as it is captured to ensure that it is valid and error-free. AI is also spotting data outliers that could be errors or indicate problems.

Security & Privacy

AI assists with security and privacy by helping ensure that only authorized people have access to sensitive data. AI models can be built that can quickly identify what data is sensitive and restrict access to it. Models can also be created that can identify users who should be authorized to access sensitive data and who should not.

AI can assist in automating aspects of data governance, making data democratization much easier to scale across the organization. IT departments are not always keen on relinquishing control of data, especially at scale, but with governance supported by AI implementation, more checks and balances are in place to ensure compliance.

Data Democratization as an Engine for Growth

IDC predicts that digital transformation spending will hit $3.9 trillion by 2027. In the coming years, hyper-automation and AI decision-making will be focal points for these investments. When data scientists have access to more data, they can build better models to drive better AI-based decision-making. Greater data literacy supported by more access to data creates a stronger data culture, leading to higher data quality as the whole organization is invested in ensuring that each data set is of the highest quality.

In the next stage of enterprise growth, decision-making will accelerate exponentially as AI undertakes many of the simple choices and experienced professionals interrogate diverse data sets to tackle complex dilemmas. Organizations that have the correct data available to the right people will be a step ahead of competitors that don’t.