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Extracting gold from your data and breaking down silos

If this all sounds like science fiction, you might want to update your model on how people’s expectations of work have shifted due to the pandemic.

Extracting gold from your data and breaking down silos
[Prostock-studio / Adobe Stock]

In McKinsey’s recent COVID-19: Implications for Business briefing, senior editor Katy McLaughlin shares a roundup of what the post-pandemic workforce expects. Alongside hybrid options, asynchronous task-centered team schedules, and social distance-designed offices, AI-powered tools are required to change the nature of work itself, upgrading efficiencies while reducing the cost of doing business.

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Aside from the fact that over 70% of workers want flexible working to continue (and don’t want to return to a soul-destroying commute), this is a fantastic opportunity to overhaul the world of work. To do this, we have to rethink everything.

Did you know that more than 50% of office pros report spending more time searching for files than on work? Why? Because static information silos destroy productivity. If time is money, then a lot of both is getting wasted at work.

What’s the fix? Embedded intelligence. But first, a cautionary tale.

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OLD WAY VS. NEW WAY

John is the CEO of a vast logistics multinational and wants everyone to return to HQ. He misses walking the hallways to ensure everyone is on the clock. His two assistants manage his calendar, which is packed with in-person meetings (he refuses to use Zoom). Unless his division heads are working late in their offices, John doesn’t feel in control, or have a sense of how the business is doing until he gets their weekly check-ins.

But there’s an issue blowing up in the supply chain. HR is struggling to find incoming talent willing to adhere to this structure. Rising stars across the board have tendered their resignation and no one has done an exit interview with them to find out why.

Unfortunately, John will be the last to hear about this, until it’s too late—probably in about 12-weeks’ time when the Q2 report has gone through four levels of approvals.

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In contrast, on a plane to Paris, Marguerite, the CEO of a SaaS company, checks her tablet device and takes a few minutes to do an entire business health check. Her AI-based interface has embedded intelligence that queries multiple company-wide data dashboards to surface insights and provide perspectives on booked revenue, sales pipeline projections, hiring, and more.

The system suddenly flags congestion in monitored workflows that will push out timelines and suggests reallocation of resources. Before touchdown, Marguerite is able to avert potential disaster in several regional hubs. Rather than waste time contacting her sector heads directly, she knows that the permissions-based system will do that for her, providing context and diagnostics for the decisions so they can pivot instantly to the new plan.

As the automated transit pulls away from the gate, Marguerite picks up her phone. Thanks to natural language processing, she uses voice commands to set new parameters on business goals by increasing pricing temporarily in one region. The system instantly grabs the data it needs to start running scenarios and delivers micro-insights on forecasts against financials, which she can review this evening, after supper with her top European customer.

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NEXT GEN AI

AI is everywhere, but not deployed to the same level as (our fictional) Marguerite—yet.

You probably use a voice-enabled assistant yourself to turn on the lights at your place, check the weather, and “Play Coltrane” to add to the evening’s ambiance. These are fairly routine requests at this point, working off the pre-programmed “if…then” protocol of all networked devices.

But, as you can see from the Marguerite scenario above, augmented analytics and embedded intelligence can liberate us not only from physical work locations, but the old ways of doing business. Remember that stat about 50% of employee’s hours wasted looking for data and files? We have to pivot to the data looking out for us instead.

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For example, within the commercial real estate sector, IoT (internet of things) is becoming fairly ubiquitous, from sensors adjusting heat and light settings and monitoring air quality. But embedded intelligence would take those devices further, into automated anomaly detection to deliver timely alerts. The data then becomes a constantly-evolving business tool.

If your data is locked up in disparate silos, it’s essentially useless on a day-to-day basis. Yes, you could draw on it to compile reports, but only if the data stores are interoperable in terms of format and nomenclature. If the data isn’t harmonized, the AI can’t ingest, tag, or categorize it. However, with a smart layer that integrates everything and can then interrogate, extrapolate, and interpret the data, you can do really cool things.

Along these lines, check out the research being done by University of Southern California’s Dr. Becerik-Gerber on “sentient environments.” Dr. Becerik-Gerber is using AI/business intelligence to make places “respond” to the people inside, promoting a more empathetic experience.

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GETTING BUY-IN

So, when it comes to implementation, what will be the key to getting buy-in from team members like John? How about a few stats to help him understand how much time, and money, he’s wasting by not implementing this new way of working? For example, Accenture reported in 2020 that data issues were costing organizations an average of more than five working days per employee.

It’s clear companies are drowning in data. But if you can quantify how much of a negative economic impact that has on your company, by sharing internal and external stats on data literacy, data-induced procrastination and stress, and IT issues, you can win over even “old way” team members.

As anyone working in IT knows, adoption is a steep learning curve and many employees quit at the first hurdle when faced with less-than-intuitive UI (user interfaces). This is why it’s crucial to understand the end user’s requirements before embarking on designing any type of business intelligence platform.

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If this all sounds like science fiction, you might want to update your model on how people’s expectations of work have shifted due to the pandemic. We’ve proved that most people can work from anywhere—as long as they have access to data and the cloud. Now we need to push that data into augmented analytics to deliver the future.


Kathy Leake is the award-winning Founder and CEO of Crux Intelligence, a next gen AI-powered business intelligence solution. 

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