Why mobile apps are now invincible parts and parcels of digital marketing strategy for brands worldwide, artificial intelligence (AI) continues to make unprecedented valuable contributions to help brands achieve their goals. The power of AI-powered mobile apps in digital marketing seems to have a far-reaching impact.
AI as technology continues to evolve and get better along with mobile apps. While mobile app user experience remains to be the quintessential aspect of modern branding and marketing exercises, AI is only helping these exercises to be more personalized and user-centric. No wonder any leading mobile app development company now relies on artificial intelligence to sharpen their mobile app marketing strategies.
Let us explain below some of the most effective ways AI-powered mobile apps are transforming digital marketing.
Creating User-Focused and Context-Aware Content
Just think of the era when artificial intelligence was not available to marketers. They needed to go through a lot of guesswork in deciphering the meaning of data. The data-driven insights about the target audience could only become possible thanks to advanced data analytics and artificial intelligence. Among the standard tips for successful mobile app marketing, it is always advised to create the right content for the right audience and at the right time. This is where AI can be tremendously helpful.
By understanding customer behavior data, trends, and patterns, the intelligent and robust AI-powered tool helps create truly personalized and target-specific content that is more likely to drive business conversion. The whole idea of personalized user experience became easier thanks to AI-powered mobile apps. As far as timing and context are concerned, digital marketers now can ensure tailored message sending schedules as per the daily routine and activities of different user groups.
Intelligent AI-Powered Chatbots
Chatbots or interactive software programs could earlier only communicate based on fixed rules. They were like the binary logic ruling course of actions before the much broader scope of fuzzy logic opened up many options between two opposite poles. Chatbots were used in websites and mobile apps for carrying out a whole array of tasks such as order processing, flight and hotel booking, personal finance management, appointment scheduling, and many more.
But the real potential of chatbots in the context of mobile apps was first realized after artificial intelligence (AI) technology penetrated the mobile apps. For the first time, the AI-powered chatbots helped marketers with automated business communication without missing user-focused personalization opportunities.
By utilizing AI bots and messaging apps, brands can easily reach out to a wider audience. Thousands of intelligent chatbots are already used by marketers across popular platforms such as Facebook Messenger, Instagram, and WhatsApp.
Unlike the earlier generation rule-based chatbots, intelligent chatbots, by going deeper into customer communication and interactions, can drastically reduce the customer support costs as most of the issues can be handled by the bots without requiring any human involvement. Only some highly complicated issues need to be flagged off for human support. The biggest impact intelligent chatbots can make is through personalized recommendations based on specific user insights.
Ensuring Revenue Growth Through Dynamic Pricing
Artificial intelligence (AI) also helps enterprises achieve higher revenue growth by setting dynamic pricing based on market conditions, expected sales volume, and the customer willingness or affordability range. As data-driven insights processed by AI technology continuously help apps about the right and effective product pricing that can ensure more business conversion and sales, this pushes business growth.
The AI technology also helps understand the precise timing and context when discounts can be more effective to push sales and contexts when discounts are less likely to impact sales. This helps marketers to decide to offer a discount when it really can convert sales.
Doing Away with Biased Notions
Irrespective of the analytical power, human beings happen to be more emotional and inclined to certain personal preferences and choices that can, in the end, can also influence their logical choices. Such human biases are held accountable for less efficient and effective decision-making.
The AI-based data-driven decision-making can help us do away with such biased decisions leading to errors. Machine-led intelligence has been proved to be more efficient in drawing precise data-driven insights to shape effective business decisions.
Creating Real-Time User Personas
User personas are created to characterize the mobile app users with specific characteristics that really matter for pushing engagement and business conversion. While earlier creating static user personas was the norm for the digital marketing manoeuvres, now more dynamic and real-time user persona creation has become more common.
Since the app world and its users continue to overlap beyond the static demographic and preference segments, the change of contexts, people and priorities now can be addressed better with dynamic and real-time user personas. Such real-time personas going beyond the rough and gross guidelines characterizing users incorporates many details and layers that can impact the way marketers can target users.
Data-Driven Targeting with Ads
AI helps mobile marketers create more personalized and target specific marketing messages based on customer behavior trends and patterns. The insights corresponding to customer preferences grasped by AI technology can also help marketers schedule messaging when they are most effective.
Just the way customer demographics, purchasing intent, usual preferences, social and online behavior, and location determine personalized recommendations, the ads can also be highly personalized or segmented based on these factors.
Apart from all the benefits that we have mentioned above, AI can also automate the mobile marketing process, making the lives of users a lot easier. Since AI primarily draws its power of precision through data-driven analytics, AI-powered marketing seldom goes wrong. In the years to come, AI will continue to help mobile apps in reshaping marketing efforts.
Extra Crunch roundup: influencer marketing 101, spotting future unicorns, Apple AirTags teardown
With the right message, even a small startup can connect with established and emerging stars on TikTok, Instagram and YouTube who will promote your products and services — as long as your marketing team understands the influencer marketplace.
Creators have a wide variety of brands and revenue channels to choose from, but marketers who understand how to court these influencers can make inroads no matter the size of their budget. Although brand partnerships are still the top source of revenue for creators, many are starting to diversify.
If you’re in charge of marketing at an early-stage startup, this post explains how to connect with an influencer who authentically resonates with your brand and covers the basics of setting up a revenue-share structure that works for everyone.
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Our upcoming TC Early Stage event is devoted to marketing and fundraising, so expect to see more articles than usual about growth marketing in the near future.
We’re off today to celebrate the Juneteenth holiday in the United States. I hope you have a safe and relaxing weekend.
Senior Editor, TechCrunch
As the economy reopens, startups are uniquely positioned to recruit talent
The pandemic forced a reckoning about the way we work — and whether we want to keep working in the same way, with the same people, for the same company — and many are looking for something different on the other side.
Art Zeile, the CEO of DHI Group, notes this means it’s a great time for startups to recruit talent.
“While all startups are certainly not focused on being disruptive, they often rely on cutting-edge technology and processes to give their customers something truly new,” Zeile writes. “Many are trying to change the pattern in their particular industry. So, by definition, they generally have a really interesting mission or purpose that may be more appealing to tech professionals.”
Here are four considerations for high-growth company founders building their post-pandemic team.
Refraction AI’s Matthew Johnson-Roberson on finding the middle path to robotic delivery
“Refraction AI calls itself the Goldilocks of robotic delivery,” Rebecca Bellan writes. “The Ann Arbor-based company … was founded by two University of Michigan professors who think delivery via full-size autonomous vehicles (AV) is not nearly as close as many promise, and sidewalk delivery comes with too many hassles and not enough payoff.
“Their ‘just right’ solution? Find a middle path, or rather, a bike path.”
Rebecca sat down with the company’s CEO to discuss his motivation to make “something that is useful to the general public.”
How to identify unicorn founders when they’re still early-stage
What are investors looking for?
Founders often tie themselves in knots as they try to project qualities they hope investors are seeking. In reality, few entrepreneurs have the acting skills required to convince someone that they’re patient, dedicated or hard working.
Johan Brenner, general partner at Creandum, was an early backer of Klarna, Spotify and several other European startups. Over the last two decades, he’s identified five key traits shared by people who create billion-dollar companies.
“A true unicorn founder doesn’t need to have all of those capabilities on day one,” Brenner, writes “but they should already be thinking big while executing small and demonstrating that they understand how to scale a company.”
Founders Ben Schippers and Evette Ellis are riding the EV sales wave
EV sales are driving demand for services and startups that fulfill the new needs of drivers, charging station operators and others.
Evette Ellis and Ben Schippers took to the main stage at TC Sessions: Mobility 2021 to share how their companies capitalized on the new opportunities presented by the electric transportation revolution.
Scale AI CEO Alex Wang weighs in on software bugs and what will make AV tech good enough
Scale co-founder and CEO Alex Wang joined us at TechCrunch Sessions: Mobility 2021 to discuss his company’s role in the autonomous driving industry and how it’s changed in the five years since its founding.
Scale helps large and small AV players establish reliable “ground truth” through data annotation and management, and along the way, the standards for what that means have shifted as the industry matures.
Even if two algorithms in autonomous driving might be created more or less equal, their real-world performance could vary dramatically based on what they’re consuming in terms of input data. That’s where Scale’s value prop to the industry starts, and Wang explains why.
Edtech investors are flocking to SaaS guidance counselors
The prevailing post-pandemic edtech narrative, which predicted higher ed would be DOA as soon as everyone got their vaccine and took off for a gap year, might not be quite true.
Natasha Mascarenhas explores a new crop of edtech SaaS startups that function like guidance counselors, helping students with everything from study-abroad opportunities to swiping right on a captivating college (really!).
“Startups that help students navigate institutional bureaucracy so they can get more value out of their educational experience may become a growing focus for investors as consumer demand for virtual personalized learning increases,” she writes.
Dear Sophie: Is it possible to expand our startup in the US?
My co-founders and I launched a software startup in Iran a few years ago, and I’m happy to say it’s now thriving. We’d like to expand our company in California.
Now that President Joe Biden has eliminated the Muslim ban, is it possible to do that? Is the pandemic still standing in the way? Do you have any suggestions?
— Talented in Tehran
Companies should utilize real-time compensation data to ensure equal pay
Chris Jackson, the vice president of client development at CompTrak, writes in a guest column that having a conversation about diversity, equity and inclusion initiatives and “agreeing on the need for equality doesn’t mean it will be achieved on an organizational scale.”
He lays out a data-driven proposal that brings in everyone from directors to HR to the talent acquisition team to get companies closer to actual equity — not just talking about it.
Investors Clara Brenner, Quin Garcia and Rachel Holt on SPACs, micromobility and how COVID-19 shaped VC
Few people are more closely tapped into the innovations in the transportation space than investors.
They’re paying close attention to what startups and tech companies are doing to develop and commercialize autonomous vehicle technology, electrification, micromobility, robotics and so much more.
For TC Sessions: Mobility 2021, we talked to three VCs about everything from the pandemic to the most overlooked opportunities within the transportation space.
Experts from Ford, Toyota and Hyundai outline why automakers are pouring money into robotics
Automakers’ interest in robotics is not a new phenomenon, of course: Robots and automation have long played a role in manufacturing and are both clearly central to their push into AVs.
But recently, many companies are going even deeper into the field, with plans to be involved in the wide spectrum of categories that robotics touch.
At TC Sessions: Mobility 2021, we spoke to a trio of experts at three major automakers about their companies’ unique approaches to robotics.
Apple AirTags UX teardown: The trade-off between privacy and user experience
Apple’s location devices — called AirTags — have been out for more than a month now. The initial impressions were good, but as we concluded back in April: “It will be interesting to see these play out once AirTags are out getting lost in the wild.”
That’s exactly what our resident UX analyst, Peter Ramsey, has been doing for the last month — intentionally losing AirTags to test their user experience at the limits.
This Extra Crunch exclusive helps bridge the gap between Apple’s mistakes and how you can make meaningful changes to your product’s UX.
How to launch a successful RPA initiative
Robotic process automation (RPA) is no longer in the early-adopter phase.
Though it requires buy-in from across the organization, contributor Kevin Buckley writes, it’s time to gather everyone around and get to work.
“Automating just basic workflow processes has resulted in such tremendous efficiency improvements and cost savings that businesses are adapting automation at scale and across the enterprise,” he writes.
Long story short: “Adapting business automation for the enterprise should be approached as a business solution that happens to require some technical support.”
Mobility startups can be equitable, accessible and profitable
Mobility should be a right, but too often it’s a privilege. Can startups provide the technology and the systems necessary to help correct this injustice?
At our TC Sessions: Mobility 2021 event, we sat down with Revel CEO and co-founder Frank Reig, Remix CEO and co-founder Tiffany Chu, and community organizer, transportation consultant and lawyer Tamika L. Butler to discuss how mobility companies should think about equity, why incorporating it from the get-go will save money in the long run, and how they can partner with cities to expand accessible and sustainable mobility.
CEO Shishir Mehrotra and investor S. Somasegar reveal what sings in Coda’s pitch doc
Coda CEO Shishir Mehrotra and Madrona partner S. Somasegar joined Extra Crunch Live to go through Coda’s pitch doc (not deck. Doc) and stuck around for the ECL Pitch-off, where founders in the audience come “onstage” to pitch their products to our guests.
Extra Crunch Live takes place every Wednesday at 3 p.m. EDT/noon PDT. Anyone can hang out during the episode (which includes networking with other attendees), but access to past episodes is reserved exclusively for Extra Crunch members. Join here.
UK’s ICO warns over ‘big data’ surveillance threat of live facial recognition in public
The UK’s chief data protection regulator has warned over reckless and inappropriate use of live facial recognition (LFR) in public places.
Publishing an opinion today on the use of this biometric surveillance in public — to set out what is dubbed as the “rules of engagement” — the information commissioner, Elizabeth Denham, also noted that a number of investigations already undertaken by her office into planned applications of the tech have found problems in all cases.
“I am deeply concerned about the potential for live facial recognition (LFR) technology to be used inappropriately, excessively or even recklessly. When sensitive personal data is collected on a mass scale without people’s knowledge, choice or control, the impacts could be significant,” she warned in a blog post.
“Uses we’ve seen included addressing public safety concerns and creating biometric profiles to target people with personalised advertising.
“It is telling that none of the organisations involved in our completed investigations were able to fully justify the processing and, of those systems that went live, none were fully compliant with the requirements of data protection law. All of the organisations chose to stop, or not proceed with, the use of LFR.”
“Unlike CCTV, LFR and its algorithms can automatically identify who you are and infer sensitive details about you. It can be used to instantly profile you to serve up personalised adverts or match your image against known shoplifters as you do your weekly grocery shop,” Denham added.
“In future, there’s the potential to overlay CCTV cameras with LFR, and even to combine it with social media data or other ‘big data’ systems — LFR is supercharged CCTV.”
The use of biometric technologies to identify individuals remotely sparks major human rights concerns, including around privacy and the risk of discrimination.
Across Europe there are campaigns — such as Reclaim your Face — calling for a ban on biometric mass surveillance.
In another targeted action, back in May, Privacy International and others filed legal challenges at the controversial US facial recognition company, Clearview AI, seeking to stop it from operating in Europe altogether. (Some regional police forces have been tapping in — including in Sweden where the force was fined by the national DPA earlier this year for unlawful use of the tech.)
But while there’s major public opposition to biometric surveillance in Europe, the region’s lawmakers have so far — at best — been fiddling around the edges of the controversial issue.
A pan-EU regulation the European Commission presented in April, which proposes a risk-based framework for applications of artificial intelligence, included only a partial prohibition on law enforcement’s use of biometric surveillance in public places — with wide ranging exemptions that have drawn plenty of criticism.
There have also been calls for a total ban on the use of technologies like live facial recognition in public from MEPs across the political spectrum. The EU’s chief data protection supervisor has also urged lawmakers to at least temporarily ban the use of biometric surveillance in public.
The EU’s planned AI Regulation won’t apply in the UK, in any case, as the country is now outside the bloc. And it remains to be seen whether the UK government will seek to weaken the national data protection regime.
A recent report it commissioned to examine how the UK could revise its regulatory regime, post-Brexit, has — for example — suggested replacing the UK GDPR with a new “UK framework” — proposing changes to “free up data for innovation and in the public interest”, as it puts it, and advocating for revisions for AI and “growth sectors”. So whether the UK’s data protection regime will be put to the torch in a post-Brexit bonfire of ‘red tape’ is a key concern for rights watchers.
(The Taskforce on Innovation, Growth and Regulatory Reform report advocates, for example, for the complete removal of Article 22 of the GDPR — which gives people rights not to be subject to decisions based solely on automated processing — suggesting it be replaced with “a focus” on “whether automated profiling meets a legitimate or public interest test”, with guidance on that envisaged as coming from the Information Commissioner’s Office (ICO). But it should also be noted that the government is in the process of hiring Denham’s successor; and the digital minister has said he wants her replacement to take “a bold new approach” that “no longer sees data as a threat, but as the great opportunity of our time”. So, er, bye-bye fairness, accountability and transparency then?)
For now, those seeking to implement LFR in the UK must comply with provisions in the UK’s Data Protection Act 2018 and the UK General Data Protection Regulation (aka, its implementation of the EU GDPR which was transposed into national law before Brexit), per the ICO opinion, including data protection principles set out in UK GDPR Article 5, including lawfulness, fairness, transparency, purpose limitation, data minimisation, storage limitation, security and accountability.
Controllers must also enable individuals to exercise their rights, the opinion also said.
“Organisations will need to demonstrate high standards of governance and accountability from the outset, including being able to justify that the use of LFR is fair, necessary and proportionate in each specific context in which it is deployed. They need to demonstrate that less intrusive techniques won’t work,” wrote Denham. “These are important standards that require robust assessment.
“Organisations will also need to understand and assess the risks of using a potentially intrusive technology and its impact on people’s privacy and their lives. For example, how issues around accuracy and bias could lead to misidentification and the damage or detriment that comes with that.”
The timing of the publication of the ICO’s opinion on LFR is interesting in light of wider concerns about the direction of UK travel on data protection and privacy.
If, for example, the government intends to recruit a new, ‘more pliant’ information commissioner — who will happily rip up the rulebook on data protection and AI, including in areas like biometric surveillance — it will at least be rather awkward for them to do so with an opinion from the prior commissioner on the public record that details the dangers of reckless and inappropriate use of LFR.
Certainly, the next information commissioner won’t be able to say they weren’t given clear warning that biometric data is particularly sensitive — and can be used to estimate or infer other characteristics, such as their age, sex, gender or ethnicity.
Or that ‘Great British’ courts have previously concluded that “like fingerprints and DNA [a facial biometric template] is information of an ‘intrinsically private’ character”, as the ICO opinion notes, while underlining that LFR can cause this super sensitive data to be harvested without the person in question even being aware it’s happening.
Denham’s opinion also hammers hard on the point about the need for public trust and confidence for any technology to succeed, warning that: “The public must have confidence that its use is lawful, fair, transparent and meets the other standards set out in data protection legislation.”
The ICO has previously published an Opinion into the use of LFR by police forces — which she said also sets “a high threshold for its use”. (And a few UK police forces — including the Met in London — have been among the early adopters of facial recognition technology, which has in turn led some into legal hot water on issues like bias.)
Disappointingly, though, for human rights advocates, the ICO opinion shies away from recommending a total ban on the use of biometric surveillance in public by private companies or public organizations — with the commissioner arguing that while there are risks with use of the technology there could also be instances where it has high utility (such as in the search for a missing child).
“It is not my role to endorse or ban a technology but, while this technology is developing and not widely deployed, we have an opportunity to ensure it does not expand without due regard for data protection,” she wrote, saying instead that in her view “data protection and people’s privacy must be at the heart of any decisions to deploy LFR”.
Denham added that (current) UK law “sets a high bar to justify the use of LFR and its algorithms in places where we shop, socialise or gather”.
“With any new technology, building public trust and confidence in the way people’s information is used is crucial so the benefits derived from the technology can be fully realised,” she reiterated, noting how a lack of trust in the US has led to some cities banning the use of LFR in certain contexts and led to some companies pausing services until rules are clearer.
“Without trust, the benefits the technology may offer are lost,” she also warned.
There is one red line that the UK government may be forgetting in its unseemly haste to (potentially) gut the UK’s data protection regime in the name of specious ‘innovation’. Because if it tries to, er, ‘liberate’ national data protection rules from core EU principles (of lawfulness, fairness, proportionality, transparency, accountability and so on) — it risks falling out of regulatory alignment with the EU, which would then force the European Commission to tear up a EU-UK data adequacy arrangement (on which the ink is still drying).
The UK having a data adequacy agreement from the EU is dependent on the UK having essentially equivalent protections for people’s data. Without this coveted data adequacy status UK companies will immediately face far greater legal hurdles to processing the data of EU citizens (as the US now does, in the wake of the demise of Safe Harbor and Privacy Shield). There could even be situations where EU data protection agencies order EU-UK data flows to be suspended altogether…
Obviously such a scenario would be terrible for UK business and ‘innovation’ — even before you consider the wider issue of public trust in technologies and whether the Great British public itself wants to have its privacy rights torched.
Given all this, you really have to wonder whether anyone inside the UK government has thought this ‘regulatory reform’ stuff through. For now, the ICO is at least still capable of thinking for them.
KeepTruckin raises $190 million to invest in AI products, double R&D team to 700
KeepTruckin, a hardware and software developer that helps trucking fleets manage vehicle, cargo and driver safety, has just raised $190 million in a Series E funding round, which puts the company’s valuation at $2 billion, according to CEO Shoaib Makani.
G2 Venture Partners, which just raised a $500 million fund to help modernize existing industries, participated in the round, alongside existing backers like Greenoaks Capital, Index Ventures, IVP and Scale Venture Partners, which is managed by BlackRock.
KeepTruckin intends to invest its new capital back into its AI-powered products like its GPS tracking, ELD compliance and dispatch and workflow, but it’s specifically interested in improving its smart dashcam, which instantly detects unsafe driving behaviors like cell phone distraction and close following and alerts the drivers in real time, according to Makani.
The company says Usher Transport, one of its clients, says it has seen a 32% annual reduction in accidents after implementing the Smart Dashcam, DRIVE risk score and Safety Hub, products that the company offers to increase safety.
“KeepTruckin’s special sauce is that we can build complex models (that other edge cameras can’t yet run) and make it run on the edge with low-power, low-memory and low-bandwidth constraints,” Makani told TechCrunch. “We have developed in-house IPs to solve this problem at different environmental conditions such as low-light, extreme weather, occluded subject and distortions.”
This kind of accuracy requires billions of ground truth data points that are trained and tested on KeepTruckin’s in-house machine learning platform, a process that is very resource-intensive. The platform includes smart annotation capabilities to automatically label the different data points so the neural network can play with millions of potential situations, achieving similar performance to the edge device that’s in the field with real-world environmental conditions, according to Makani.
A 2020 McKinsey study predicted the freight industry is not likely to see the kind of YOY growth it saw last year, which was 30% up from 2019, but noted that some industries would increase at higher rates than others. For example, commodities related to e-commerce and agricultural and food products will be the first to return to growth, whereas electronics and automotive might increase at a slower rate due to declining consumer demand for nonessentials.
Since the pandemic, the company said it experienced 70% annualized growth, in large part due to expansion into new markets like construction, oil and gas, food and beverage, field services, moving and storage and agriculture. KeepTruckin expects this demand to increase and intends to use the fresh funds to scale rapidly and recruit more talent that will help progress its AI systems, doubling its R&D team to 700 people globally with a focus on engineering, machine vision, data science and other AI areas, says Makani.
“We think packaging these products into operator-friendly user interfaces for people who are not deeply technical is critical, so front-end and full-stack engineers with experience building incredibly intuitive mobile and web applications are also high priority,” said Makani.
Much of KeepTruckin’s tech will eventually power autonomous vehicles to make roads safer, says Makani, something that’s also becoming increasingly relevant as the demand for trucking continues to outpace supply of drivers.
“Level 4 and eventually level 5 autonomy will come to the trucking industry, but we are still many years away from broad deployment,” he said. “Our AI-powered dashcam is making drivers safer and helping prevent accidents today. While the promise of autonomy is real, we are working hard to help companies realize the value of this technology now.”
Entrenched Data Culture Can Pose Challenge to New AI Systems
By John P. Desmond, AI Trends Editor
Companies established for a long time—decades or even a century or more old—with thousands of employees in many business units globally, with information systems built over many years on multiple platforms, have entrenched data cultures that may pose challenges for implementing AI systems.
Data culture refers to the expectation that data will be used to make decisions and optimize the business, making a company data-driven. A data-driven company can be rolling along peacefully, with complex business processes and operations under control and doing the job. Users may have access to the data they need and be encouraged to present their analysis, even if the insights are unwelcome.
Then someone asks if the company can do it like Netflix or Amazon, with AI algorithms in the background making recommendations and guiding users along, like a Silicon Valley startup. Might not be able to get there from here.
“These great companies may have built enormously successful and admirable businesses,” stated Tom O’Toole, professor at the Kellogg School of Management, writing recently in Forbes.
However, many legacy companies have IT organization structures and systems that predate the user of data analytics and now AI. The data culture in place may be resistant to change. In many firms, culture is cited as a primary challenge to the successful implementation of AI.
“Established organizations are too often fragmented, siloed, and parochial in their data use, with entrenched impediments to information sharing,” stated O’Toole, who before working in academia was chief marketing officer at United Airlines. Questions to established authority might not be welcome, especially if the top executive doesn’t like the answers.
To replicate the Silicon Valley approach, the author had these suggestions:
Get comfortable with transparency. Data that previously resides only within one department is likely to have to be shared more broadly across the leadership team. Business performance data needs to be transparent.
Heighten accountability. Greater accountability follows increased transparency. Data needs to be provided to demonstrate that a particular strategy or product launch is effective.
Embrace unwelcome answers. A data analysis can challenge conventional assumptions, for example by showing performance was less than had been believed, or that the conventional wisdom was not that smart.
“Creating a data culture is an imperative for continuously advancing business performance and adopting AI and machine learning,” O’Toole stated.
Survey Shows Concern that Data Quality Issues Will Cause AI to Fail
Nearly 90% of respondents to a survey by Alation, a company that helps organizations form an effective data culture, are concerned that data quality issues can lead to AI failure.
“AI fails when it’s fed bad data, resulting in inaccurate or unfair results,” stated Aaron Kalb, cofounder and chief data and analytics officer, in an account on the Alation blog. “Bad data, in turn, can stem from issues such as inconsistent data standards, data non-compliance, and a lack of data democratization, crowdsourcing, and cataloging.” Survey recipients cited these reasons as the main reasons for AI failures.
The company’s latest survey asked organizations how they are deploying AI and what challenges they are facing doing so. The results showed a correlation between having a top-tier data culture and being more successful at implementing AI systems.
Data leaders who have deployed AI cite incomplete data as the top issue that leads to AI failures. “This is because when you go searching for data to create the models—be it for product innovation, operational efficiency, or customer experience—you uncover questions around the accuracy, quality, redundancy, and comprehensiveness of the data,” Kalb stated.
Aretec, a data science-focused firm that works to bring efficiency and automation to federal agencies, helps clients deal with legacy data by leveraging AI services themselves to integrate and optimize huge and diverse datasets.
In a post on the Aretec blog, the issues they consistently see that impede the implementation of AI systems are:
Data Fragmentation. Over time, the data needed to support operations winds up fragmented across multiple data silos. Some can be outside an agency or stored with private companies. Fragmented data eventually results in “islands” of duplicated and inconsistent data, incurring infrastructure support costs that are not necessary.
Data inconsistencies. Many government agencies need to aggregate data records coming from a variety of sources, records not always in a consistent format or content. Even when rigid standards are applied, the standards are likely to evolve over time. The longer the records go back, the greater the chance for variance.
Learning curves. Many challenges arising from legacy data management are cultural, not technical. Highly-skilled employees have spent years learning how to do their job efficiently and effectively. They may see any proposed change as compromising their position, thus having a negative impact on their productivity and morale.
NewVantage Survey Find AI Investment Strong, Success Fleeting
A newly-released survey from NewVantage Partners found that Fortune 1000 companies are investing heavily in data and AI initiatives, with 99% of firms reporting investments. However, the ninth annual update of the survey finds that companies are having difficulty maintaining the momentum, according to a recent account in the Harvard Business Review.
Two significant trends were found from the 85 companies surveyed. First, companies that have steadily invested in Big Data and AI initiatives report that the pace of investment in those projects is accelerating, with 62% of firms reporting investments of greater than $50 million.
The second major finding was that even committed companies struggle to derive value from their Big Data and AI investments and from the effort to become data-driven. “Often saddled with legacy data environments, business processes, skill sets, and traditional cultures that can be reluctant to change, mainstream companies appear to be confronting greater challenges as demands increase, data volumes grow, and companies seek to mature their data capabilities,” stated the author, Randy Bean, the CEO and founder of NewVantage Partners, who originated the survey.
Only 24% of responding firms said they thought their organization was data-driven in the past year, a decline from 37.8% the year before. And 92% of firms reported that they continue to struggle with cultural challenges related to organization alignment, business processes, change management,, communication, people skills sets, resistance and a lack of the understanding needed to enable change.
“Becoming data-driven takes time, focus, commitment, and persistence. Too many organizations minimize the effort,” stated Bean.
One recommendation by the study authors was for companies to focus data initiatives on clearly-identified business problems or use cases with high impact.
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