Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion…

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Everyone wants to implement Artificial Intelligence (AI) and Business Intelligence (BI) solutions. AI alone is anticipated to generate $15.7 trillion in GDP by globally 2030, and as this market grows, AI and BI will shift from industry buzzwords, to key market differentiators, before eventually becoming the new normal in the corporate landscape.

Yet bringing AI and BI on board is a big leap if it’s your first major data project. Stibo Systems’ Claus Jensen, Head of Emerging Technology, comments on the role of MDM as a vital foundation to implement emerging data technology.

Most CEOs don’t trust their own data.*

Let that sink in for a moment.

Almost every business is looking to data solutions to fuel the next phase of growth and innovation. AI and BI are firmly on the agenda, yet a report by Forbes Insights and KPMG found 84% of CEOs are concerned with the quality of the data they’re basing their decisions on.

That’s a significant disconnect. Businesses at board level want to implement ‘next generation’ data projects, but don’t trust the data that will be fed into them. For CDOs and other data leads, this presents a difficult situation. They need to meet demand for cutting-edge data projects, knowing that there is a certain level of mistrust in the data at their disposal.

For many CDOs, that mistrust isn’t limited to the CEO. Think about the data you are currently processing: how confident are you that it’s being accurately sourced, entered, saved, stored, copied and presented? How well do you know that data journey once it leaves your sphere of control? Are you certain that a single source of truth is being maintained?

The data gold rush

It may only be major data breaches that make the headlines, but in the global gold rush for data, too many businesses fail to accurately extract, store and interpret data.

Mistakes are made at every stage in the process – in fact, so bad are we at processing data, a report by Royal Mail Data Services claims that around 6% of annual revenue is lost through poor quality data.

It’s equally bleak in the US, where Gartner’s Data Quality Market Survey puts the average cost to US business at $15 million per year.

Despite this, we’re rapidly moving the conversation from data capture to artificial intelligence (AI), business intelligence (BI) and connected devices (IoT) – and for good reason.

Putting aside the issue of bad data (we’ll come back to that), businesses now have access to more data than they can handle – according to SAS’ Business Intelligence and Analytics Capabilities Report, 60% of business leaders struggle to convert data into actionable insights, and 91% of companies feel that they are incapable to doing it quickly enough to make useful changes. 

Business Intelligence and Analytics Capabilities Report

In large businesses, where data streams are blended from many sources, machine learning can help data scientists monitor figures to flag outliers, irregularities and noteworthy patterns.

Once flagged, business leaders can use BI to bring those patterns to life, helping pave the way for the most appropriate, and profitable, action.

Stibo Systems’ Head of Emerging Technology, Claus Jensen, believes it’s only a matter of time before we see AI regularly used within business product features – with machine learning automating tasks thanks to effective data interpretation.

Jensen and his team are working at the forefront of data: building master data management solutions in conjunction with AI and BI. “We’re entering into a new era of data analytics,” says Jensen. “Data scientists aren’t going away, but they can do more and more high-level work as certain use cases are solved by AI.” 

One of these use cases is machine learning-based auto classification. “For retailers onboarding thousands and thousands of new products every month, it’s really time consuming for them to have the vendor categorise the product into the vendor taxonomy.

“Machine learning can automate this based on product description and image.”

Running before we can walk

As exciting as this sounds, businesses eager to install new uses for data often face significant challenges: their data isn’t watertight, or it’s siloed, often both.

In a piece penned for the Financial Times, Professor of Economics at Stanford Graduate School of Business, Paul Oyer, wrote: “Smart managers now know that algorithms are as good as the data you train them on.” In other words, AI (and analytics for that matter) can only ever be as good as the date you feed it.

Which brings us back to the question of trust. What needs to happen for CEOs to trust their own data?

While there’s no single answer to this question, a master data management (MDM) solution is a good place to start.

“You can think of MDM as the foundation, a layer, that provides a single source of the truth for data,” explains Jensen. “Analytics and machine learning is only useful if the data you’re working on is accurate. That’s where MDM comes in; it ensures information presented, and actions taken, are based on fact and reality.

“Otherwise, business analytics is just a nice and colourful way to look at bad data, and what’s the point in that?”

To find out more about how MDM can turn data into business value through actionable insights, forming a solid foundation to AI and BI, visit https://www.stibosystems.com/solution/embedded-analytics-platform.

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