11 Data Trends of 2022, According to the CEO of Dataddo

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Ten years in the past, most organizations noticed knowledge and analytics as a nice-to-have. Today, it’s turning into a core enterprise perform throughout industries, with 97% of Fortune 1000 companies investing in data initiatives.

Data is a treasure trove of alternative for many who know the way to leverage its potential. But it’s additionally a black field of threat for many who don’t, each in phrases of cybersecurity and compliance with ever-evolving rules.

Knowing the following eleven tendencies will assist corporations keep shrewd and agile in the new period of data-driven enterprise.

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1. The period of “big data” is fading out and the period of “small and wide data” is fading in.

In 2011, McKinsey & Company printed an article stating that the new era of big data was upon us. What they meant was that corporations tended to have a couple of, very complicated programs that have been beginning to amass “mountains” of knowledge.

Nowadays, due to the proliferation of software program as a service (SaaS), many companies are amassing a number of small “hills” of knowledge throughout a large panorama of purposes. In 2015, the average number of SaaS applications being utilized by organizations worldwide was 8. In 2020, it was 80. And in 2021, it was 110. 

Graph showing increase in average number of SaaS applications being used by organizations worldwide

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The datasets from these cloud apps are smaller and less complicated, but extra focused and thus ship higher insights to decision-makers.

Gartner predicts that 70% of organizations can be paying the majority of their consideration to small and vast knowledge by 2025.

The SaaS increase is totally altering the manner knowledge is being dealt with inside organizations, which leads us to the subsequent development.

2. Data architectures have gotten composable.

Among the cloud apps popping up in all places are specialised instruments for knowledge integration, storage, and evaluation. This reality has set in movement a development in the direction of the “composability” of knowledge architectures, which means that companies choose and select the instruments that comprise their architectures at any given time, as dictated by their altering wants.

According to Gartner, “composable technology architecture is a foundation for digital enablement of business.” This development was accelerated by COVID-19, which made organizational agility a key worth for many organizations.

Just like large knowledge is popping into small and vast knowledge, vertical scaling of knowledge stacks is shifting to horizontal scaling, whereby corporations construct “chains” or pathways for knowledge processing as a substitute of piling sources on prime of current programs. Here is an instance of what a composable knowledge structure may seem like:

composable data architecture including Dataddo, Looker, and Apache

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It can be value mentioning that composable knowledge architectures are cheaper since they are often scaled at any tempo and—as a result of they’re primarily based on standardized purposes supplied by exterior distributors—they require much less upkeep from in-house groups.

3. The realm of knowledge analytics is turning into self-service.

Traditionally, dashboards have been constructed by IT groups which are decoupled from the enterprise professionals who want to view them. When the enterprise professionals require new info, they submit a request to their IT workforce, which may take wherever from hours to months to fulfill.

This association is a tall barrier to the flexibility that companies more and more want to keep a aggressive benefit.

Hence the rise of self-service analytics, whereby non-technical finish customers can arrange knowledge pipelines and customise dashboards independently of knowledge engineers. Aside from stress to shorten time to insights, this development is being pushed by three elements:

  1. Business intelligence (BI) instruments have gotten extra user-friendly.
  2. Non-technical finish customers have a need to enhance their knowledge literacy.
  3. The look of no-code knowledge integration instruments.

The marketplace for self-service BI instruments is already extraordinarily aggressive, and is anticipated to develop 15% annually until 2026. There can be a transparent push from distributors to make them as user-friendly as attainable.

Below is an instance of a user-friendly dashboard in Zoho Analytics:

Zoho Analytics dashboard showing revenue per employee, active customers, and other metrics

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A recent, global survey by Accenture of 9,000 workers from corporations throughout industries signifies that many workers would really like to be extra knowledge literate—37% acknowledged that knowledge literacy coaching would enhance their effectivity and 22% felt that it might alleviate stress.

And lastly, there are actually a number of no-code knowledge integration instruments that permit enterprise customers pull knowledge from any supply and ship it to any vacation spot for additional processing and evaluation.

4. More departments are adopting knowledge and analytics as a core enterprise perform.

Data and analytics has the potential to information enchancment of nearly any course of in any enterprise division. But till lately, its improve in significance has been most seen in advertising and marketing and gross sales departments.

Now, nevertheless, knowledge and analytics is turning into a driving power behind the actions of different departments. This is mirrored by the adoption of self-service BI tools across fields like HR, operations, finance, and even education.

Additionally, BI capabilities are actually embedded in lots of department-specific instruments, like recruitment platforms, which use AI to quicker determine candidates, in addition to demand planning software, which makes use of predictive analytics to assist plan operations.

As knowledge and analytics continues to develop into a core enterprise perform, corporations will extra continuously blend their data across departments to create an interconnected internet of superior insights.

5. “Citizen Data Scientist” is an rising function in corporations.

Data scientists and engineers are good at knowledge, however they usually don’t have the area data that’s wanted by particular person departments to make that knowledge actionable.

So, what we are going to see increasingly of this 12 months is “citizen data scientists,” i.e. professionals in non-data departments who’ve some data of knowledge evaluation, however whose total skilled data is better-aligned with their respective division. Here’s some duties {that a} citizen knowledge scientist would find a way to carry out: 

Roadmap of tasks that a citizen data scientic would perform

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These professionals know what knowledge their departments want to monitor and the way to visualize it utilizing no-code instruments. However, they themselves don’t construct the knowledge fashions. 

It is essential to observe the time period “citizen data scientist,” coined by Gartner, will unlikely develop into the title of a brand new place marketed throughout job portals round the world. Instead, the duties thereof can be written into the job descriptions of different positions.

Global corporations like BP are already reaping nice advantages from citizen knowledge scientists.

6. Data high quality is turning into a serious concern.

As extra customers begin working with knowledge, the higher the potential is for errors in knowledge to be proliferated to downstream programs.

Imagine a content material supervisor whose Hubspot account is linked to Google Analytics (GA). Every week after publishing a brand new weblog article, they verify their GA dashboard and, due to a typical error in the measurement script, the article has double the views most articles have after their first week.

The supervisor then begins publishing extra, comparable articles, solely to discover that they aren’t producing the identical outcomes. Oops!

Situations like this have set in movement a twofold development:

  1. Companies and workers are studying to pay extra consideration to knowledge high quality. Next time this content material supervisor sees a dramatic spike in views of their GA dashboard, they are going to return to their native Hubspot dashboard to guarantee that the numbers match.
  2. Technologies for catching errors and anomalies are creating quickly. Using AI, they mathematically outline outliers by analyzing very long time sequence. Anomaly detection tools now cater to all kinds of enterprise use circumstances, however since they work greatest when fed massive datasets, they aren’t a silver bullet. The consideration of customers is subsequently essential.

7. Data standardization for AI workloads is on the rise.

The incontrovertible fact that we’re getting our knowledge from a higher quantity of less complicated programs and instruments implies that it now is available in a greater variety of constructions. This is an issue for AI-based evaluation.

Calendar dates are a very good instance of why. One system could file them as MM.DD.YYYY whereas one other could file them as DD.MM.YYYY. Humans can simply see previous these variations, however machines can’t.

So, so as to make knowledge analyzable by machines, it should be standardized or “transformed.” This is extraordinarily essential as a result of these days there’s a main increase in the use of AI-based purposes throughout industries, most notably in IT and telecom, banking and finance, retail, healthcare, and marketing

Traditionally, transformations have been the area of builders, who execute them periodically on massive volumes of knowledge. This will stay related for a lot of use circumstances, however due to the rising expanse of small and vast datasets that want to be processed rapidly and continuously by non-technical professionals, no-code ETL instruments similar to Dataddo are the manner ahead.

Dataddo landing page featuring value proposition that it can connect all data sources

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8. The demand for any-to-any integration is rising.

As increasingly specialised instruments seem, so will the want to combine all of them with each other. This is as a result of, sarcastically, the knowledge integration instruments that are actually extensively praised for eliminating knowledge silos are literally new knowledge silos.

To illustrate, let’s say that your accountant spends so much of time working instantly in Hubspot CRM, and that they need to extract invoicing knowledge from Hubspot and pair it with exterior knowledge for superior insights. They can of course do that by sending the bill knowledge to a warehouse, mixing it with the different knowledge, after which viewing the leads to a BI instrument.

But this may finally develop into a trouble, and the accountant will begin questioning whether or not it’s attainable to remove the BI instrument as a silo and examine all the knowledge instantly in Hubspot.

This is the place any-to-any integration is available in. It empowers actors in any respect ranges to create their very own, single supply of reality by sending knowledge from any supply or vacation spot to any supply or vacation spot. Here’s an illustration of integrations amongst an ERP, CRM, website, advertisements campaigns, and social media platforms:

illustration of integrations among an ERP, CRM, website, ads campaigns, and social media platforms

Any-to-any integration functionality is so new that the terminology for it’s not but solidified, however it’s generally referred to as knowledge activation or reverse ETL.

There are only a few instruments for it on the market, however this may change inside the subsequent 12 months or so.

9. How knowledge is ruled is turning into a key enterprise concern.

The democratization of knowledge, whereas empowering, has introduced a wave of new challenges for enterprises.

According to Nick Halsey, CEO of knowledge authorization firm Okera, in an article in Forbes, these challenges embody “too much data from too many sources […] an unprecedented level of cyberattacks [… and] hybrid work environments that have employees moving between home and office, between personal and work devices.”

Companies are subsequently dealing with mounting stress to deal with the concern of knowledge governance, which in observe means 2 issues:

  1. Ensuring knowledge high quality.
  2. Ensuring knowledge safety.

If knowledge is embedded in all decision-making processes, it wants to be full and constant. And if it’s being accessed by a rising quantity of customers, they want to be the proper customers, at the proper time and place.

This is forcing corporations to craft knowledge governance insurance policies that search a stability between centralization and decentralization. Centralization means higher safety and better knowledge high quality, however much less energy from knowledge; decentralization means higher potential for impartial, knowledgeable decision-making, however greater threat of non-compliance and poor knowledge high quality.

Below is an illustration of the hub and spoke model of knowledge governance:

illustration of hub and spoke model of data governance

The knowledge governance market is anticipated to develop from 2.1 billion USD in 2020 to 5.7 billion USD by 2025—an almost threefold improve!

10. The concern of knowledge safety and compliance is gaining prominence.

Whereas knowledge governance is how corporations select to handle their knowledge, knowledge compliance is how they should handle their knowledge. With knowledge turning into increasingly plentiful, compliance with acts like GDPR in the EU and shopper knowledge privateness legal guidelines in the US is a serious concern.

The seriousness of this concern in Europe is mirrored by the spiking annual GDPR superb totals. In 2020, fines totalled €306.3 million. In 2021, they exceeded €1 billion, with the largest superb to date—€746 million—being imposed on Amazon by the courts of Luxembourg. 

graph showing month over month increase in total GDPR finesin 2021

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There is no federal data privacy law in the US yet, and solely three states have complete shopper privateness legal guidelines in place, however most different states have some kind of privateness laws in the pipeline, so we will solely anticipate that American enterprises will quickly don’t have any selection however to watch fastidiously what they acquire and the way they share it.

Gartner even predicts that by 2030, 50% of B2C businesses around the globe will cease retaining buyer knowledge due to not possible compliance prices.

11. Customer knowledge platforms will lose relevance.

Customer knowledge platforms (CDPs) are all-in-one software program packages that acquire and mix first- and third-party knowledge from a range of channels to give companies a 360-degree view of their prospects.

While they do allow integrations and visualizations of knowledge, they’re primarily designed for advertising and marketing use circumstances and include preset knowledge fashions that don’t supply the flexibility companies will come to depend on.

This is an issue for 2 causes:

  1. Businesses will want to find a way to adapt their knowledge architectures to meet rapidly altering wants.
  2. Businesses will increasingly need to combine all their instruments with each other to remove knowledge silos.

CDPs will hold their place on markets the place gamers have extra fastened wants. But as cloud-based programs and specialised instruments proceed to proliferate, these all-in-one options are possible to stagnate on markets whose gamers want composability.

Seeing Clearly Through the Fog

The rising abundance of knowledge is totally altering the manner selections are made throughout industries—by prime administration all the manner down to frontline workers. And the instruments on the market at this time make it simpler than ever and to construct and keep an information structure that may arise to market volatility.

Yes, improper knowledge governance opens the door to regulatory non-compliance and cyber threats. But the advantages of knowledge leverage far outweigh the difficulties that include it. 

The alternatives are large, and companies that don’t take them threat getting caught in the fog.


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