How to build a trusted Analytics Foundation using data governance?

With the maturity of the information age, data has turned into the most potent resource enterprises. Businesses have embraced digital transformation, frequently risking their reputations on insights extracted from collected data. Although decision-makers try to implement AI and potential of data to drive businesses into the future, many underestimate the pitfalls of poor data governance. If they cannot trust the data within their organisation, how can stakeholders and customers realise they are in good hands?

Businesses often prioritise data access and analysis over governance, in search of instant analytical insights. Leaders cannot be confident about their decisions are rooted in fact and reality without ensuring the data is trustworthy.

Many businesses mistake adequate data security for good data governance. Even if they are linked, each requires its own attention. To remain ahead of the competition and coming regulation, companies need a solid plan for both.

Here are the 5 ways to build a trusted analytics foundation:

Safeguard your business with data awareness and governance strategy:

Marketers want to put data at the centre of their decision-making processes to reduce missteps and increase their competitive advantage. Three keys to building the ideal data governance strategy are listed below:

Gather data silos so that you get the entire picture for your organization:

Data sources are increasingly exponential. With the expansion of social and IoT data and the progression of digital channels, there are adequate unique data types to provide insights to enterprises. The only problem is that those individual sources are frequently funnelled through different segments of the business with each representing a small portion of a unique buyer profile. Collating these individual pieces together needs connecting data from multiple sources across the industry. However, more data in one location isn't always useful. Dispensing many types of heterogeneous information into a single place can muddy the waters and resist insights to come out. To get meaningful data, you need to access the right dataset.

Create a catalogue for faster access:

Building a 'singular source of truth' is just the first step to creating a long-lasting advantage. Even though you have data in a simplified landscape, you require knowing where to look for critical information. Data repositories can be taken up as libraries. They could be stocked with thousands of separate pieces of information. Users and automated systems would never know where to look without thousands of individual components. The cataloguing system doesn't allow you becoming overwhelmed, but you can easily track down the information you're hunting for.

Prepare the ever-evolving data landscape:

Most of the chief data officers are being charged with discovering new revenue streams for data. This indicates they hold accountable for pinpointing better management of data and focusing more on quality assurance.

Today's data landscape calls for technology, regulations, compliances, and constant evolution. It is more complicated for a business that operates in multiple countries, work in highly regulated industries or do business with several data partners around multiple cloud eco-systems. It's pivotal to plan and be flexible for tomorrow's technologies and potential regulations while creating your data governance strategy.

Even though AI brings everything to a business, consumers still demand an opt-out button. Ensuring compliance builds trust, driving to new revenue opportunities and reductions in risk of litigation and unethical use.

Data Governance for Competitive advantage :

In the era of AI, unequal data division between organizations will be more glaring, switching from competitive edges to critical business advantages. Companies that have not dominated data governance will find their AI based on unsound insights.