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7 data trends to expect in 2022: deepfakes, greater privacy awareness and yes, the metaverse

Plus, one wish for the death of Excel.

Data center. (Photo by Manuel Geissinger from Pexels)
This is a guest post by Patrick Callahan, the cofounder of Wilmington-based data science company CompassRed. It originally appeared on CompassRed's website and is republished here with permission.
It’s really easy to predict five years from now. No one will ever remember you made that prediction. But it’s really hard to predict the next year. Your vision and dreams need to be a little closer to reality.

What makes 2022 even more difficult are the events of the past two years and all that’s impacting our world in business and as humans.

But we still need to try. Our business, in data, is important to this world. People make life and death decisions based on what we provide. Families are affected by being the source of information for leaders and decision makers. We are a humble culture at CompassRed; our mission and our responsibility is not taken lightly.

Asking our team what they see as the year of 2022 in the data space, here are some of the thoughts that rose to the top:

1. Greater awareness in data privacy and protection

Cambridge Analytica was an important event in our world. It exposed what’s possible at one end of the spectrum when it comes to the use of your data. Last year, as Facebook ended up in Congress and events around the election were front and center, people became more aware of how their data is being used and its potential impact.

As a result, consumers became more aware of their rights to privacy, leading businesses to be more aware of both their responsibilities as keepers of that data as well as their customer’s views of the expectations around that use. “Consumer trust” is an important concept that is coming out of the legal terms and conditions and into the marketing and branding that companies will begin to require.

We have seen Apple’s move start this off with the implementation of greater controls on devices for consumers and limitations on advertisers collecting data. Customer centricity is always in style. As more companies that dominate the tech industry respond to their consumers’ desire for more control over their data, downstream businesses that rely on that data will need to adjust their business models and data models alike. We’ll be watching to see how more wins for consumer privacy further upend the foundation that digital advertising rests upon.

2. Keeping the narrative for your KPIs

The last year or two have made it tempting for companies to over-rationalize measurement of their progress on key objectives, particularly as the world goes through seismic change. “COVID negatively impacted our customer acquisition!” or “Online sales tripled (but not due to our marketing plan)!” While these scenarios highlight some aspect of reality, neither warrant the need to change the metrics the business plans are built upon. We hope to see a more rationalized KPI strategy that focuses on the results from actions we are in control over and their relevant results.

3. More open, more affordable and more accessible data warehouses

The enterprise data warehouse has been around for decades — but it’s always been out of reach for the middle market, and all too often fails to provide value. It’s easier than ever to move and transform data, with open-source tooling, more APIs than is possible to imagine, and with more talent in the work space that understands how to use these tools.

With one part discipline and governance, one part inexpensive storage options like Snowflake, the modern data warehouse can evolve. The key is willingness to address the hard issues, like business data that is inconsistent across silos. With some cross-enterprise communication and governance, you can quickly get meaningful data into the hands of business analysts wielding their data visualization tool of choice, opening a path to insight.

4. Deepfakes get exposed (or uncertain)

At the tail end of 2021, we saw the scare of deepfakes, the practice of digitally substituting someone’s face or likeness through AI, as real. It’s still real and it’s not going away, as development will be driven by commercial agendas. The big movie houses started using the technology to reduce the cost of movie production during COVID, which means there are not just nefarious reasons for the practice of producing deepfakes. This also means that, as the technology becomes more accessible, businesses will not be the only ones creating deepfakes. Individuals creating content on YouTube and other video platforms may well create deepfakes to attack competitors or to further their agendas.

To counter the development, there has also been new developments in the spotting of deepfakes, such as Facebook’s recent development of AI that can reverse engineer deepfakes. If we don’t get this right, the damage that is done could be even greater than the damage that is being done today. We believe there will be further advancements to try and counter this development. We can even imagine a world where images and video are certified by the blockchain — perhaps opening up another level of trust via the global digital distributed ledger.

5. Automation for the masses

We paid particular attention last year to robotic process automation (RPA). It’s been on the radar for a few years now, and with UiPath making it to the public market, it seems validation came quickly. One thing we do know about RPA is that it can be expensive — but worth the price. We expect to see more service providers pop up that meet the middle market need. RPA is great for our industry as it allows computers to reliably handle the repetitive while humans tackle the hard stuff. It takes advantage of “Real AI.”

6. Information distortion and data distortion

One in the same — and it’s worrisome. There is a book by Simon Rogers called “Facts are Sacred” which offers an intelligent diagnosis of the importance of data, data storytelling, and its impact. The title alone is a statement on how critical (even more-so now) facts are to decision-makers. We believe this will become a focal point in 2022 as more organizations look toward validating the information that underlies analytics through data indicators such as source, freshness, version control, and other metadata.

Through simple acts of fostering trust, understanding, and integrity, we’re reflecting on best practices for ourselves — and in partnership with our clients — to advance data governance at the organizational level and reinforce data literacy for end users. Removing subjectivity, bias, or other distortions will remain top of mind as long as we continue to rely on data to deliver information that inspires action.

7. Application of AR/VR to the business world

Something we are really interested in seeing is whether AR/VR in data analysis begins to pick up steam. Last year we explored Spatial, Flow Analytics, and some of the other solutions that took AR/VR into the collaborative data analysis space to determine if there was success in reducing the time to insight.

When viewing the collaborative virtual environment in general, we can’t help thinking of the era of the fax machine and the need for a network effect to really get it going. With sales of some of the headsets over the holiday season increasing, we might be witnessing that change. When Apple jumps into the AR/VR world this year, there will be a larger tipping point.

There is also something else going on in the AR/VR space that fascinates us. NFTs, blockchain, and digital coinage (i.e. Bitcoin) have found a new medium. Articles point to real estate firms, and big-name brands (i.e. Sotheby’s) all staking out real estate as if it’s the Western Land Claims of the 18th century. Investors are looking for an early stake that is similar to the early grabs of Bitcoin, and in the metaverse, they may not be far from it.

And as a bonus:

Wishful thinking: The beginning of the end of Microsoft Excel

More than one of our clients has started out a conversation with the statement, “I wish Excel would just die.” The number of spreadsheets in an organization can be an anchor to holding back progress. In a distributed Excel environment, the proliferation of non-validated business logic and lack of central data governance breeds a lack of trust in the data, causing teams to silo around particular workbooks and custom Excel solutions rather than pushing for a unified data pipeline.

As college graduates, are all trained up on business intelligence — we feel more will shift to less reliance on the spreadsheet and more will be demanding the analysis of BI — but the death of Excel still is at the point of fighting against human nature. It’s sadly not going away.

Companies: CompassRed

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