Kevin Mandia testifies during a Senate Intelligence Committee hearing on Capitol Hill on February 23, 2021 in Washington, DC.

Drew Angerer | Getty Images

As the recent Log4j breach demonstrates, U.S. businesses and government organizations have been taking a pounding from cybercriminals. It's coming in the form of ransomware, data breaches, distributed denial-of-service (DDoS), and other damaging attacks.

Now, many are saying enough is enough.

"I think more people are taking advantage of the United States and our openness and our true global workforce than in any other nation," said Kevin Mandia, CEO of cyber security company Mandiant, in a session on cybersecurity at CNBC's recent Technology Executive Council (TEC) Summit in New York.

Rather than simply bolstering traditional defenses such as firewalls and waiting to be the next potential victim of a cyber assault, companies are beginning to take a more proactive approach to security. They're going on the offensive, actively seeking out cyber threats and disabling them before they can wreak havoc on systems and networks.

The increase in ransomware, one of the more insidious and damaging types of attacks, is a major driver for going on the offensive. An August 2021 report from research firm International Data Corp. showed that more than one-third of organizations worldwide have experienced a ransomware attack or breachthat blocked access to systems or data in the previous 12 months.

Weary of the ongoing assaults, organizations are fighting back.

"What you're starting to see is a coordinated national response maybe even a coordinated international response because [of] ransomware," Mandia said. "Quite frankly everybody hates it except for people doing it and the people harboring those who do it."

A good example of an effective coordination was the takedown earlier this year of REvil, a ransomware-as-a-service operation linked to Russia. A group of countries and law enforcement organizations used technical and legal methods to knock the operation off the Internet.

While it's uncertain exactly how REvil was taken out of commission, the collaboration by multiple entities is a positive development in the effort to minimize or eliminate threats, Mandia said. With ransomware becoming a national security issue as well as a criminal one, the U.S. needs to consider bringing military assets to bear in the fight to stop these attacks, he said.

"We can do a lot of different things rather than just constantly making it a clean-up on aisle nine after the crime," he said. Military action "doesn't mean drone strikes, it means proportional response" to the attacks, he added. That can only happen when the sources of the attack are identified.

A strong step would be the creation of a national "doctrine" that states how the U.S. will deal with creators of ransomware and other cyber threats, as well as the nations that harbor them, Mandia said.

"There could be some vagueness to that doctrine, but people need to know that the nation is going to have a coordinated response" to attacks, he said. "There comes a time where you just can't stand there and take it anymore."

Technology executives expect the high level of external threats to continue, with TEC members responding to a recent CNBC survey saying that state-sponsored cyber warfare (32%) and criminal organizations (25%) remain the most dangerous cyber threats. They give the Biden administration decent marks in its cybersecurity efforts so far, with less than 5% of TEC members saying Biden has done a "poor" job on cybersecurity during his first year in office. Thirty-nine percent of respondents said the Biden administration has done a "good job," while another 9% described its efforts as "excellent." Another 35% said the administration has done an "average job" when it comes to cybersecurity.

On an individual basis, businesses can take steps to get ahead of cybercriminals. For example, they can deploy threat hunting and threat intelligence tools or services.

With threat hunting, companies' security teams proactively search through networks and systems to find and isolate advanced threats that can evade older security tools such as firewalls, intrusion detection systems, and security information and event management products. The latest threat-hunting offerings can be at least partially automated via technologies such as machine learning, so companies don't need to rely on time-consuming manual hunting processes.

Threat intelligence is also effective at detecting and stopping cyber attacks. Security teams can tap resources such as open source intelligence, social media intelligence, and services from a number of vendors to track existing and emerging threats.

Threat intelligence lets companies develop a more proactive approach to cyber security, in large part because it's predictive. Another key benefit of using such resources is that they promote the sharing of knowledge and experiences among the cyber security community, which supports the idea of a broad effort to "attack" cybercriminals before they strike.

Identity technologies such as multi-factor authentication (MFA) and advanced endpoint security tools, both of which support the concept of "zero trust" security, can also help companies be more proactive.

"Multi-factor authentication is a phenomenal thing for breaking lateral movement," Mandia said. MFA is the first step toward a zero-trust network, he says.

The recent TEC survey finds more than half (52%) of respondents saying their firms are in the initial stages of implementing zero trust security; another 27% say they already have implemented the approach and are seeing the benefits.

Monitoring of networks and endpoints, such as mobile devices, will continue to get increasingly sophisticated and effective with the addition of AI, machine learning, and data science, because security teams can better predict when suspicious activities are actually the beginning of an attack.

Aside from technology, employeetraining in security awareness is essential for building a proactive security program.

Tactics such as phishing, malware, and social engineering in many cases succeed because employees are not properly trained or not trained at all in how to recognize these incidents.

With the proper mindset, tools, and training, companies can create a proactive cyber security program that thwarts cybercriminals before they can do damage.

Bob Violino, special to CNBC.com

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We're starting to see a national response to ransomware, says Mandiant CEO - CNBC

By Chris Hillman, Teradatas Data Science Director EMEA and Chris Newbery, Teradatas Industry Consultant Retail/CPG EMEA

A recentMcKinsey&Co articleoutlines the challenges retail and CPG companies face when trying to break the code of digital analytics. They, in common with our own consultants, perceive the need for the retail and CPG leaders of the future to successfully leverage analytics at speed and scale to drive performance. By their estimates, leaders have already delivered more than triple the total return to shareholders than the laggards in this area!

REALISING VALUE FROM ANALYTICS IN RETAIL

We all recognise the drivers: unprecedented changes to consumer behaviour, radically intensified competition, highly pressured margins and rapidly evolving sales channels all accelerated by the COVID pandemic. McKinsey reflects our thinking on two further points of specific relevance to the Chief Data Officer: the need to create a simplified enterprise data architecture, and to give flexibility to quickly deploy and reuse analytics across the organisation. However, the challenge we encounter every day is that moving from understanding the requirements, to implementing solutions, is never as easy as companies hope. We see a big discrepancy in the industry between recognition of the need to change (which most understand is required) and the ability to deliver the transformation (with many unable to do this anywhere near quickly enough). McKinsey suggests that only 20% of CPG businesses are realising value from analytics at scale, and that reticence to invest and scale-up quickly is at least partly to blame.

HARMONISING DATA AT SCALE

Looking at just two dimensions of McKinseys matrix illustrates that with a strategic road map that builds on proven frameworks, the CDO can quickly show significant return on investment and provide the basis for multiplying benefits. Many retailers and CPGs have difficulty in harmonizing data at scale and creating production pipelines that unify data to deliver reusable features. Simply put, too much data resides in silos, only feeding individual departmental insights. Data is seldom shared or re-used, and looking for data, even within a specific area of the business, is difficult and time consuming. Attempting to integrate data sets across stores, geographies, ERP systems, product lines or departments is often all but impossible. These factors are why highly skilled and in-demand Data Scientists spend up to 80 percent of their time just preparing data. Worse, once all that effort has been made, the results are often forgotten, and the next project starts from scratch all over again!

AnEnterprise Feature Storeimplemented on Teradata Vantage can help retailers and CPGs overcome these bottlenecks. Prepared, integrated and performant data features are catalogued and stored in a referenceable library from where they can be reused by other Data Scientists across the entire business; thereby reducing huge amounts of time/money/data duplication, as well facilitating a more efficient processes that will deliver wider implementations of AI and Machine Learning to support multiple business cases.

Crucially, the Enterprise Feature Store approach accumulates value. Start with a small discrete project which will prove the concept and establish the first features in the store. Subsequent projects can then reuse these as they build out the store. The more projects and the more features, the more value is driven. The more value is driven, the more they get developed, and you are rewarded with a virtuous cycle of return on investment.

ALIGN AND ACCELERATE ANALYTICS DEPLOYMENT

The second of McKinseys dimensions to highlight is the need to align analytics vision, talent, and tools. Many in the sector continue to struggle to deploy analytic models with the flexibly required. Different Data Scientists use different languages to create their models, using bespoke pipelines that lead to technical debt (what happens when they leave?) and make it hard to deploy at scale. Instead, huge swathes of data are copied and moved to silos where they increase overall costs and quickly become out of date.

The TeradataAnalytics 123approach provides a formula to overcome these hurdles. The Enterprise Feature Store provides a trusted repository of proven features, while delivering the security, privacy and governance required to build trust. Data Scientists can then use their preferred modelling language(s) to develop the analytics using trusted features, and seamlessly deploy/execute them in Teradata Vantage on live data. Data movements are minimised, and real-time dashboards, automated actions and all other advanced analytics can run from live data, placed at the heart of the decision-making process.

SIMPLE, EFFICIENT, REUSABLE

Working closely with the biggest retailers and CPGs across the world, Teradata has helped them take these vital steps. A typical engagement starts with using integrated data from two or three functions or systems to answer a specific business issue for example, Teradata worked with a French grocer to better understand price drivers on a selection of SKUs and quickly increased profit margins by 5-10%. Once proven, the concept can then be extended to additional lines and locations. The features created are then re-used to drive complementary business analysis and so the benefits multiply. In many cases these solutions leverage existing data, technologies and platforms simply by providing a better unified and more efficient analytics pipeline.

It is clear to most retail and CPG businesses that current manual and fractured approaches to analytics within their businesses are not sustainable. Mainstream retail/CPG businesses are deploying a few million predictive models at best but to compete in this new environment they will need to scale and deploy hundreds of millions of models in production. Breaking the code of analytics is not a once and done action; it is not a sprint, but more of a relay race. Sequential projects will piece together the crucial elements of an enterprise-wide data platform and enable fast, flexible deployment of analytics across the business.

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From Strategy to Action: How to Break the Code of Analytics at Scale in Retail and CPG - Global Banking And Finance Review

The College of Engineering is pleased to announce that the Multidisciplinary Data Science Scholarship program will host an in-person information session 5-6 p.m. Jan. 25, 2022.

Sponsored by a grant from the National Science Foundation, the MDaSScholarship program is designed to increase the graduation rate of underrepresented STEM undergraduate students interested in careers in data science.

The program is open to all STEM disciplines science, technology, engineering and mathematics but preference will be given to students enrolled in the data science program and disciplines related to data science (industrial engineering, computer science and computer engineering, physics, biology, and mathematics with a specialization in statistics).

MDaS aims to establish recruitment and retention programs for new data science majors and other related STEM disciplines to help meet growing workforce needs for qualified STEM graduates with data science skills. The program brings faculty and business leaders together to mentor students interested in data science and other related STEM disciplines, in career development initiatives. It also helps to provide skills sought after, with the aim of improving the graduation rate of students interested in careers related to data science.

Data science combines the fields of computer science, mathematics, statistics and information systems with a focus on the generation, organization, modeling and use of data to make scientific and business decisions. To learn more about data science at the University of Arkansas, please visit datascience.uark.edu.

To claim your spot for the Tuesday, Jan. 25, information session about the scholarship program, register here.

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Multidisciplinary Data Science Scholarship Program to Host Information Session - University of Arkansas Newswire

We polled 19 experts and received 31 data science and analytics predictions for 2021, in an attempt to help you make the best business decisions.

As part of Solutions Reviews third-annual #BIInsightJam, we called for the industrys best and brightest to share their data science and analytics predictions for 2022. The experts featured here represent the top data science and analytics solution providers with experience in this niche. Data science and analytics predictions have been vetted for relevance and ability to add business value as well. These are the best predictions from the dozens we received. We believe these are actionable and may impact a number of verticals, regions, and organization sizes.

Note: Data science and analytics predictions are listed in the order we received them.

Mainstream AI and Deep Learning

As the toolset for AI applications continues to evolve, machine learning and deep learning platforms have entered the mainstream and will attain the same level of maturity as specialized data analytics. Just like we currently see a plethora of fully integrated managed services based Apache Spark and Presto, in 2022, we will see vertical integrations emerging based on the likes of PyTorch and Tensorflow. MLOps for pipeline automation and management will become essential, further lowering the barriers and accelerating the adoption of AI and ML.

Data in Motion is the Next Automation Holy Grail

For automated decisions and machine learning, both AI technologies that rely on the input of data, the data itself remains far from malleable. All too often, massive amounts of enterprise data is difficult to scale, store, and use in an actionable way. Until AI and automation technologies can better master the flow of data, advancements will be slow-moving. In the years ahead, as enterprises master how to tap into data in motion, we will see greater innovation in automation that enables decision-making based off real-time, data-backed insights.

Predictive Analytics Will Drive New, Emerging Use Cases Around the Next Generation of Digital Applications

The technology will become more immersive and embedded, where predictive analytics capabilities will be blended seamlessly into the systems and applications with which we interact. Predictive analytics will drive use cases in next-gen apps like metaverse applications (convergence of digital and physical worlds, powered by technologies such as IoT, digital twins, AI/ML, and XR) and the next generation of composable applications.

Enterprises Will Employ Adaptive Learning in BotVille

Bots and automation are all the rage. However, while many companies have become pretty good at automating existing processes, few have learned from that automation. You need a heads-up display for RPA with AI-driven insights. Adaptive, incremental, dynamic learning techniques are growing fields of AI/ML that, when applied to the RPAs exhaust, can make observations on the fly. Patterns of behavior are continuously observed. These dynamic learning technologies help business users see and act on AHA moments and make smarter decisions.

Enterprises Will Discover the Big AI Lie

92% of companies are invested more in AI in 2021, yet just 12% are deploying it at scale, down from last year. Whats going on? How can companies be spending MORE on AI but getting LESS from it? There are many non-obvious factors at play: culture, tools, bias concerns, fear, and automation grace the top of the list. In 2022, firms must meet these challenges head-on with a cultural approach to model operationalization to better manage, track and optimize algorithms. Only then will data science move from the playground to the battleground.

ModelOps is Hot

Working from home in the pandemic has accelerated collisions and collaborations between teams of data scientists, DevOps, and model ops developers to get data science apps into production. Emerging from this is a focus on converting ad-hoc processes into a controlled environment for managing low code and code first components, processes for data flows and model connections, along with rules, actions and decisions. Continuous analysis of models actually in operations is also in focus to assess ROI of the data science app, model drift and model rebasing.

ML Engineers are now in the middle of this configuring deployment scenarios in hybrid cloud environments, working with data scientists, data engineers, business users and devops teams; and with app dev and design teams.

Analytics Must Move Beyond Insights and Into Actions and Decisions

Todays fast-changing business climate demands real-time visibility and up-to-the-minute recommendations from data and analytics. To survive the post-pandemic world, organizations need to be able to predict whats going to happen next based on the data they have; and develop more discipline around decisions and actions. Processes for measuring impact and closing the decision intelligence loop will sharpen their focus.

More Open-Source Behind Analytics & AI

As the momentum behind the Open Data Lake Analytics stack to power Analytics & AI applications grew over the past year, well see a bigger focus on leveraging Open Source to address the limitations around flexibility and cost when it comes to traditional enterprise data warehouses. Open source cloud-native technologies like Presto, Apache Spark, Superset, and Hudi will power AI platforms at a larger scale, opening up new use cases and workloads that arent possible on the data warehouse.

Augmented BI Will Make Data Democratization a Reality

The biggest barrier to data democratization was allowing business and non-technical users to access raw data for analysis. Augmented analytics capabilities such as Natural Language Processing (NLP) and Natural Language Querying (NLQ) will allow business users to get answers to important questions without having to work with the data directly. This helps companies bypass complexities in issuing and managing user-level permissions to raw data.

Contextual Embedded Analytics Will be Key to Successful Analytics Implementation

The chances of an organization acting uponinsightis much higher when presented directly within the business application than when it is presented in a standalone BI software. This is mainly because of two aspectscontextual availability and short reach to the decision-making audience. For example, wheninsighton project efficiency is present right within a project management software, it makes it easy for project managers to relate it to their daily work and put measures in place to fix inefficiencies.

Unstructured Analytics

In 2022, with powerful technologies available to them, organizations will invest more in unstructured analytics. To date, most business intelligence has been conducted using structured data; however, there are countless problems that cannot be answered by these clean-cut numbers. Burgeoning people analytics teams are offered a new means of assessing uniquely human situationstalent acquisition, workforce sentiment, productivity, etc.by analyzing the textual, conversational, and communicative data created by the workforce each day. These emails, files, and collaboration data speak to the human side of the enterprise that has long remained out of reach.

Unstructured Data Analytics Workflow Solutions Will Emerge

Processing and indexing petabytes of unstructured data is today largely a manual effort. Large organizations employ legions of data professionals to search, catalogue and move this data so it can be ingested by analytics tools and manipulated. Theres a dire need to simplify and automate these processes. Solutions that index files easily across multiple file and cloud silos and automate the systematic data movement will be on the rise.

Also, data analytics solutions for unstructured data might be verticalized, so they are sector specific or applications specific. For instance, medical images and how you interpret them is a contextual event requiring specific knowledge of clinical data sets. Organizations are creating custom workflows consisting of cloud-based analytics tools like Amazon Comprehend for PII detection along with manual data movement and data lakes. The time is ripe for commercial data management solutions which can enable easy search of specific data sets across a global enterprise and stream this data continually to systematically automate the workflow of unstructured data analytics.

The CAO Will Eclipse the CDO

While many companies today have a chief data officer, in 2022 we will see more enterprises establish chief analytics officer or chief data and analytics officer roles. Elevating analytics reflects an evolving understanding of data science and machine learning as the ultimate functions that turn data into business value, and increasingly core to company-wide strategy.

Democratization of ML Through Up-Skilling Will Make More Analysts Comfortable with Code

For over 20 years, different products have promised to enable advanced analytics with no-code or drag-and-drop user interfaces. The latest wave of this trend will lose enthusiasm in favor of companies investing to upskill their workforce. Analytical programming languages like Python and R will become more table stakes (especially with the rise of data science degree programs in secondary education), just as Excel and SQL became a decade ago.

Unpredictable Business Conditions Will Accelerate Adoption of Model Monitoring

Model monitoring, already critical in a post-pandemic economy, will become essential. The continued volatility of unpredictable business factors, from supply chains to extreme weather, will greatly accelerate the need for businesses to continuously monitor how well their models reflect the real and rapidly changing world of their customers.

Organizations Will Redefine What it Means to Build a Culture of Analytics

For too long, business leaders have assumed that upskilling their workforce with data classes/certifications and investing in self-service tools would lead to a data-driven organization. They are finally ready to admit that its not working. Self-service BI does not close the skills gap. Not everyone has time or interest in becoming a data analyst or data literate, especially now in todays post-COVID landscape where teams are understaffed and people are valuing their time differently in and outside of work. In 2022, organizations will redefine what it means to build a culture of analytics and change the paradigm by bringing insights to workers in a more digestible way turning to methods and solutions like embedded analytics that wont require them to learn new skills or invest additional time.

The Most Data-Driven Organizations Will Combat Tool Fatigue by Bringing Data to Workers Where They Are

The rise of work-from-home and the digital acceleration brought on by the pandemic means that more people than ever are using different tools in different places to do their jobs from email to collaboration software like Slack and Teams to the many point solutions needed to get work done across departments. As a result, workers everywhere are experiencing tool fatigue, distractions and inefficiencies from jumping around from software to software or being forced to use tools that dont fit into their personal workflow. Rather than investing in data/analytics solutions that add yet another tool to the mix, well start to see more organizations in 2022 delivering insights to employees directly within their workflows via embedded analytics (for example, directly within Slack, Teams, etc.). In this environment, workers can make data-driven decisions without thinking twice and without any disruptions.

Automation Turns Prescriptive Analytics Into Prescriptive Guidance

For years we heard that the future of analytics will go beyond descriptive analytics (what happened) and predictive analytics (what will happen) to prescriptive guidance (what to do about it). AI combined with automation will finally make this possible by dynamically combining relevant data and alerting knowledge workers to take action, in advance, before an event occurs. Customer Service reps will be notified to reach out to potentially angry customers before they even call in. Sales leaders will react immediately to dips in revenue pipeline coverage due to upstream activities without waiting until the end of the quarter. Retail managers can optimize inventory before items sell out by combining more than just sales data, such as purchasing patterns of other items, external market trends, and even competing promotional campaigns. Prescriptive analytics will finally evolve from telling us just where the numbers are going, to helping us make smarter, proactive decisions.

Decision Intelligence Makes Inroads for Enterprise-Wide Decision Support

Organizations have been acquiring vast amounts of data and need to leverage that information to drive business outcomes. Decision intelligence is making inroads across enterprises, as regular dashboards and BI platforms are augmented with AI/ML-driven decision support systems.

In 2002, decision intelligence has the potential to make assessments better and faster, given machine-generated decisions can be processed at speeds that humans simply cannot. The caveat machines still lack consciousness and do not understand the implications of the decision outcome. Look for organizations to incorporate decision intelligence into their BI stack to continuously measure the outcome to avoid unintended consequences by tweaking the decision parameters accordingly.

Organizations Embrace Composable Data and Analytics to Empower Data Consumers

Monolithic architectures are already a thing of the past but expect even smaller footprints. As global companies deal with distributed data across regional, cloud and data center boundaries, consolidating that data in one central location is practically impossible. Thats where composable data architecture becomes paramount and brings agility to data infrastructure. Data management infrastructure is extremely diverse and usually every organization uses multiple systems or modules that together constitute their data management environment. Being able to build a low-code, no code data infrastructure provides flexibility and user friendliness, as it empowers business users to put together their desired data management stack and makes them less dependent on IT.

In 2022, expect organizations to accelerate building composable data and analytics environments that can bring faster business value and outcomes.

Small and Wide Data Analytics Begin to Catch On

AI/ML is transforming the way organizations operate, but to be successful, it is also dependent on historical data analytics, aka big data analytics. While big data analytics is here to stay, in many cases this old historical data continues to lose its value.

In 2022, organizations will leverage small data analytics to create hyper-personalized experiences for their individual customers to understand customer sentiment around a specific product or service within a short time window. While wide data analytics is comparatively a new concept and yet to find widespread adoption given the pace at which organizations are making use of unstructured and structured data together expect to see small and wide data analytics to gain better traction across organizations as we enter 2022.

The Rise of the Just in Time Data Analytics Stack

Theres a small, but fast growing, segment of the data analytics space that is focused on new approaches to the enterprise stack, including continuing to move all the things to the cloud. However, the hybrid multicloud imposes requirements of its own most notably the ability to manage and analyze data no matter where it lives in the hybrid multicloud environment.

Startups like Starburst, Materialize.io, Rockset, and my own company Stardog develop platforms that are designed to query, search, connect, analyze, and integrate data where it lays without moving or copying it, in a just-in-time fashion. In a world where the number of places that data may be residing in storage is increasing, rather than decreasing, expect to see enterprises reach for data analytics solutions that are not coupled to where data lives. This trend will accelerate in 2022 as data movement between storage systems will continue to be removed from the stack in order to accelerate time to insight.

Knowledge Graph-Enabled Data Fabrics Become the Connective Tissue for Maximizing Analytics Value

Gartner indicates that data fabric is the foundation of the modern data management platform with capabilities for data governance, storage, analytics, and more. Relying on traditional integration paradigms involving moving data and manually writing code is no longer acceptable as data scientists and data engineers spend almost 80 percent of their time wrangling data before any analytics are performed. Shrewd organizations looking to adopt this approach are realizing that the centerpiece of a properly implemented data fabric is an enterprise knowledge graph, which compounds data fabrics value for better, faster, lower-cost analytics while hurdling the data engineering challenges obstructing them.

2022 will be the year organizations adopt enterprise knowledge graph platforms to support their data fabrics that use a combination of graph data models, data virtualization, and query federationalong with intelligent inferencing and AIto eliminate this friction by simplifying data integration, reducing data preparation costs, and improving the cross-domain insights generated from downstream analytics.

Business Users Will be Empowered to Become Data Analysts

Enterprises will empower business users to become data analysts by applying well-trained natural language processing (NLP) and machine learning technologies, and implementing richly curated data catalogs to unleash the power of complex analytics. Organizations with integrated data strategies will provide their employees with the tools that allow them to gain data analyst superpowers by tapping into vast amounts of data and drive business results. This improves the productivity of business users and eliminates bottlenecks caused by the reliance on data analysts to find and analyze trusted data within their organizations, making the process more prolonged and arduous than necessary.

Move from Dashboards to Data-Driven Apps

If humans, even augmented by real-time dashboards, are the bottleneck, then what is the solution? Data-driven apps that can provide personalized digital customer service and automate many operational processes when armed with real-time data. In 2022, look to many companies to rebuild their processes for speed and agility supported by data-driven apps.

Industrial Data Scientists Emerge to Facilitate Industrial AI Strategy

The generational churn occurring in the industrial workforce will inspire another trend: the widespread emergence of industrial data scientists as central figures in adopting and managing new technologies, like industrial AI and just as importantly, the strategies for deploying and maximizing these technologies to their full potential.New research revealed that while 84 percent of key industrial decision-makers accepted the need for an industrial AI strategy to drive competitive advantage and 98% acknowledged how failing to have one could present challenges to their business only 35% had actually deployed such a strategy so far.

With one foot in traditional data science and the other in unique domain expertise, industrial data scientists will serve a critical role in being the ones to drive the creation and deployment of an industrial AI strategy.

The Conversation Around Data for AI Will be Prioritized

The discussions around data for AI have started, but they havent nearly received enough attention. Data is the most critical aspect for building AI systems, and we are just now starting to talk and think about the systems to acquire, prepare, and monitor data to ensure performance and lack of bias. Organizations will have to prioritize a data-first approach within an enterprise architecture in 2022 to enable AI and analytics to solve problems and facilitate new revenue streams.

The Need for Data in Decision Making Has Never Been Greater

As the demand for business intelligence (BI) software rises, so do new advancements giving users the ability to analyze and make intelligent decisions without any programming knowledge. Not only can enterprises gain a competitive advantage, but todays BI is being used to address supply chain issues and save lives. The pandemic emphasized the importance of relying on data, rather than hunches, as the world became dependent on COVID-19 visualizations to steer us out of the crisis. Government agencies and health experts are using big data analytics tools to understand, track, and reduce the spread of the virus. BI helps health experts identify vaccine supply chain issues, virus hotspots, COVID-19 rates, and more, all in real-time. The next-gen BI may change the way we determine trends and ultimately, it may even be able to predict the future.

AI Technologies Associated with Data Science Will be Used Increasingly by Data Engineers

Data engineers will increasingly use AI-based tools in their day-to-day work. To support this, more analytics vendors will incorporate AI programmatic capabilities in their platforms, opening up new opportunities for data engineers. This will also blur the line between data engineering and data science, providing new opportunities for innovation.

The Most Transformational Analytics Use Cases Will Come From Citizen Analysts

Due to their domain expertise, proximity to the business, and availability of new (tools|technologies), citizen data analysts will become the most important and influential individuals who work with data. This will lead to an explosion of new ideas and practical applications for data, marking the next big turning point for the industry.

Just-in-Time Supply Chain Failures Will Fuel Meteoric Rise of Just-in-Time Data Analytics

Faced with a full-blown supply chain crisis, companies will have to address long-standing issues in their data pipelines bottlenecks and other fragilities that prevent teams from gaining the visibility into supply chains they need to survive the decade. No longer held back by the gravity of legacy models, systems and approaches, companies will embrace innovative new solutions in a bid to make just-in-time data analytics a reality for their business.

Tim is Solutions Review's Editorial Director and leads coverage on big data, business intelligence, and data analytics. A 2017 and 2018 Most Influential Business Journalist and 2021 "Who's Who" in data management and data integration, Tim is a recognized influencer and thought leader in enterprise business software. Reach him via tking at solutionsreview dot com.

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31 Data Science and Analytics Predictions from 19 Experts for 2022 - Solutions Review