Solutions Review’s Expert Insights Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, IKASI co-founder and CEO Anthony Chong offers a comparson between business intelligence vs. personalization.

Over the last 20 years, business intelligence (BI) software has become ubiquitous in enterprises, as it provides great ways for organizations to visualize their data, track key performance indicators, and find compelling insights. However, this technology is becoming increasingly outdated, as more enterprises are moving away from traditional BI tools in favor of those that offer high levels of personalization.

The major drawback of BI was that it promised to improve business performance through insights, which are broad conclusions or takeaways garnered from aggregating data and understanding the so-called “average” customer. Like early online clickbait ads promised to reveal “one simple trick,” these insights were supposed to provide key information that could determine whether a customer was profitable or not. For instance, BI software might produce an insight that tells you if a customer buys three products in a row, then they’re your customer for life. Or, if customers under 40 years old spend twice as much as customers over the age of 40. Unfortunately, this approach was incapable of describing what was really going on.

Business Intelligence vs. Personalization

Why Business Insights Alone Are Ill-equipped

The insights produced by BI tools (and generated from a single variable) are incapable of recognizing the complexity of individuals, making them functionally useless for a business. Not every person will behave the exact same way and not every customer will be described by these generalized statements. There’s usually a more complex story, and many more caveats needed to describe what’s really going on.

Worse, drawing sweeping insights from averages of data can lead organizations to conclusions that are the opposite of reality. BI software was, and is, not equipped to handle the messy realities of customer behavior.

Enter the Age of AI-powered “Hyper Personalization”

Instead of using BI tools to analyze customers in groups based on one particular bucket or antidote, modern artificial intelligence (AI) solutions enable organizations to draw conclusions based on each individual person.

These AI-driven approaches allow organizations to take into account a multitude of variables to create a personalized analysis and a more accurate reading of that customer’s individual behavior. AI provides the ability to collect multidimensional data on each customer, and understand individuals’ unique stories at the scale of hundreds of thousands of customers. Instead of flattening all that data to glean broad insights, businesses are conducting personalized analyses for individuals and are capitalizing on the insights all the way down to their unique behavioral data. Looking at what they’ve interacted with, for example, organizations can see how often a person is coming into a store or engaging with a brand. Rather than trying to pattern-match by comparing their behavior with that of other customers, they can be much more responsive to the individual and other changes in their specific behaviors.

By looking at each individual as the fundamental unit of analysis, businesses are drawing conclusions from deeply personalized experiences, and can individualize customer journeys. As opposed to earlier BI offerings, AI is unveiling an unprecedented level of understanding that helps to allocate internal resources and budgets more efficiently, because decision making is based on that person’s preferences and behaviors. Rather than relying on single-variable-based insights on a broad group, businesses and customers now understand that to truly appeal to customers, they need to undergo a mentality shift, and focus on the multifaceted variables that contribute to the specific individual’s behavior.

The post Business Intelligence vs. Personalization: Why the Time Has Come to Get Hyper Personal appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Regardless of your profession, we’re all familiar with the triple constraint of speed, quality and price in business.

Conventional wisdom says we can have only two at the most. If the priority is high speed and high quality, you’ll pay a high price. If you want good quality at a good price, then speed will suffer.

Forward-thinking data analysts and consultants specializing in AI, machine learning, predictive and prescriptive modeling will advise their clients to prioritize quality and accuracy over speed and price.

Unfortunately, AI technology innovators are scrambling to be the first at the cost of everything else. In the new space race for AI dominance, technologies are being released before they’re ready for prime time.

Rapid AI Advancements

Case in point, Auto-GPT, is an experimental open-source interface to GPT-4 and GPT-3.5, which can complete tasks autonomously. Compared with OpenAI’s ChatGPT, Auto-GPT automates multi-step projects that would have required many prompts for ChatGPT to accomplish. In addition, Auto-GPT can access websites, search engines, apps, software and services—both online and local. The ability to gather external data allows Auto-GPT to self-evaluate, verify collected data, remove inaccurate and sub-par information and create a new task to get better data.1

Max Tegmark, cofounder of the Future of Life Institute and a leading AI Safety researcher at MIT, says these types of experiments are violating safety norms for AI development, such as:2

• Teaching AI systems to code so that AI can’t create other software.
• Teaching AI systems about manipulating human psychology.
• Giving AI systems access to the Internet.

The Future of Life Institute recently posted an open letter to “Pause Giant AI Experiments” which received more than 11,000 signatures, including Elon Musk, Steve Wozniak and Tristan Harris of the Center for Humane Technology.3

We need to keep in mind that even with the most bleeding-edge AI iterations, they are simply tools. The technologies lack critical thinking and ethical judgement. Humans must stay in the driver’s seat to harness the power of generative AI and machine learning.

Maximizing Machine Learning in Data Analytics

There’s no denying the value that data scientists can derive from AI, machine learning, large language model (LLM) and natural language processing (NLP). Machine learning is behind the big data analytics each time we get personalized recommendations from Netflix. Google’s predictive text in emails and optimized directions in Google Maps are also being powered by machine learning.

Starbucks’ Digital Flywheel program merges digital and physical customer interactions around rewards, personalization, payments and orders. Using big data, AI and NLP, Starbucks delivers highly personalized emails based on customers’ past purchases. Instead of the typical few dozen emails sent monthly with offers to the broad Starbucks audience, the digital flywheel produces more than 400,000 personalized weekly emails featuring different promotions and offers.4

Other ways LLMs and NLP can be used by data analysts include creating code and applications to analyze information and automate processes for gathering, formatting and cleansing data. The tools can define the charts, diagrams and infographics to be included in reports, as well as offer guidance on compliance and regulation to make certain data operations are legal, unbiased and ethical.5

Will Generative AI Take Data Analysts’ Jobs?

Given generative AI’s fast-paced advancements and ongoing massive layoffs at leading firms, it’s understandable that some data analysts may be concerned about security in their career.

If AI can collect and analyze data within minutes, plus recognize patterns and compile the information into formats that are easily understood by colleagues and clients, are data jobs at risk?

The bottom line is that even sophisticated LLMs and NLP tech can’t solve complex problems. They don’t have critical thinking and strategic planning capabilities. So even as AI becomes more established and gains widespread accessibility, data scientists, analysts and consultants will continue to be vital to long-term business success.

AI Tips for Data Analysts

Here are three tips for staying up to date on the latest AI innovations:

1. Assign a team to integrate machine learning into your organization’s standard operating procedures that will benefit clients.
2. Make certain team members embrace lifelong learning, receive regular training and keep certifications updated.
3. Invite data analysts to attend webinars and industry events to ensure your organization keeps up with the newest trends.

By staying open-minded and open to learning, data analysts can harness AI technology to be more productive while maintaining quality and affordability.

The post 3 Tips for Data Analysts To Stay Ahead of AI Advancement appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of June 30, 2023, including a number of features from the Databricks Data + AI Summit 2023.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

Analytics and Data Science News for the Week of June 30, 2023

Alteryx Launches New Automated Intelligence Features on Snowflake Data Cloud

Initiating the Snowflake Partner Connect integration creates a free trial of the Alteryx Analytics Cloud Platform for new users, automatically configuring a Snowflake connection to Alteryx. Snowflake users can now access the Alteryx Analytics Cloud Platform right from their Snowflake account, enabling customers to drive insights in just minutes.

Read on for more.

Amplitude Announces New Snowflake-Native Amplitude Offering

This application marks Amplitude’s first warehouse-native offering. Warehouse-native Amplitude will help more organizations uncover the behavioral insights they need to make better product decisions without leaving their data platform of choice.

Read on for more.

AtScale Partners with Dataiku on Enterprise AI

By combining Dataiku’s single data and AI platform with AtScale’s universal semantic layer and AI-Link, organizations will be able to improve data science productivity with business-vetted features for AI model training and feature management. The integrated capabilities enable users to incorporate AI insights directly into common business intelligence tools like Tableau, PowerBI, and Excel, on their cloud warehouse of choice, including Snowflake, Databricks, and Google BigQuery.

Read on for more.

Crux Expands Partnership with Databricks on Financial & Alternative Data Sources

Crux serves as an enabler for data engineering and bridges the gap between consumers of external data and cloud data warehouses, working with hundreds of external data vendors to address complex integration issues, build and maintain pipelines for a variety of datasets, and resolve intricate ETL challenges.

Read on for more.

Cloud Software Group Secures Strategic Partnership with Midis Group

The partnership provides Cloud Software Group with the local resources customers need to support their transformative technology journey and the scale required to expand its reach in these regions. A leading technology partner providing managed IT services and consultancy, system integration, cloud and data center capabilities, infrastructure, software, and hardware solutions, the Midis Group serves customers through a network of 170 companies across 70 countries.

Read on for more.

Databricks Set to Acquire MosaicML, Makes Slew of Product Enhancements at Data + AI Summit

LakehouseIQ uses generative AI to understand jargon, data usage patterns, organizational structure, and more to answer questions within the context of a business. The upcoming release of Delta Lake 3.0 introduces Universal Format (UniForm), which allows data stored in Delta to be read from as if it were Apache Iceberg or Apache Hudi. These capabilities unify previously siloed data systems under the Databricks Lakehouse Platform.

Read on for more.

New Immuta Integration with Databricks Enables Secure Data & AI Workloads

These updates include a new native integration with Databricks Unity Catalog that connects customers with Immuta’s latest platform capabilities providing localized Sensitive Data Discovery, enhanced security and access control for artificial intelligence (AI) workloads, and enhanced Data Security Posture Management.

Read on for more.

Qlik and Talend Extend Transformation, Quality & Analytics Tools on Databricks

Qlik’s data integration and analytics solutions, when combined with Talend’s cloud data quality and transformation capabilities, create a unique portfolio of solutions that help Databricks customers scale their ability to find, transform, trust and analyze data.

Read on for more.

Sigma Computing Names Snowflake Business Intelligence Partner of the Year

Sigma was purpose-built for the modern cloud and is a leading BI Partner for Snowpark’s recently announced machine learning-powered functions, providing a native integration within the Sigma platform. Sigma Custom Functions continue the journey of making analytics available to end users by enabling administrators to create aliases that encapsulate complex logic as simple, documented function names.

Read on for more.

Snowflake Partners with NVIDIA on Generative AI in the Data Cloud

NVIDIA and Snowflake’s collaboration represents a new opportunity for enterprises. It will enable them to use their proprietary data — which can range from hundreds of terabytes to petabytes of raw and curated business information — to create and fine-tune custom LLMs that power business-specific applications and services.

Read on for more.

ThoughtSpot Set to Acquire Mode Analytics for $200 Million

With the acquisition, ThoughtSpot’s ARR will grow to over $150M, while doubling its customer base. With very little customer overlap, this transaction will create new opportunities for each company to bring their respective products to customers, while further scaling Mode across ThoughtSpot’s international market presence and broad channel and partner alliances.

Read on for more.

Expert Insights Section

Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Solutions Review Set to Host Alteryx for Exclusive Spotlight Webinar on July 27

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Read on for more.

For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Analytics and Data Science News for the Week of June 30; Updates from Alteryx, Databricks, Qlik & More appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of June 23, 2023.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

Analytics and Data Science News for the Week of June 23, 2023

Databricks Announces New Lakehouse Apps & Expanded Marketplace

Lakehouse Apps will enable over 10,000 Databricks customers to unlock the value of their data in the Lakehouse. Customers will have easy access to a wide range of powerful applications that run entirely inside their Lakehouse instance, using their data, with the full security and governance capabilities of Databricks.

Read on for more.

Dataiku Adds New Generative AI Capabilities & Use Case Collection

This new approach will allow enterprises to move out of an artisanal approach of building custom Generative AI projects and into the industrial-scale development and deployment of these use cases, moving out of the research lab and into the corporate office.

Read on for more.

Qlik Drops New Suite of OpenAI Connectors to Enhance Analytics Experience

Qlik recently showcased the potential of combining Generative AI and Qlik with a demonstration at QlikWorld that used ChatGPT to drive insights within Qlik Cloud. The session showed how Generative AI can complement a wide range of use cases, from adding external data sets to analysis, expanding context with natural language readouts, and asking questions that deliver new insights from data.

Read on for more.

Lovelytics Nets Strategic Investment from Databricks Ventures

Founded in 2017, Lovelytics provides strategy, data engineering, and technology implementation services that enable global enterprises to optimize the management, governance and utilization of organizational data. Through partnerships with technology solution providers Databricks, Tableau and Alteryx, among others, Lovelytics helps businesses better manage and use structured and unstructured datasets to develop data-based intelligence.

Read on for more.

Microsoft Adds Two New Multi-Tasking Updates to Power BI

The new caching experience makes these quick transitions faster. It works when you navigate back to Power BI within 60 seconds of navigating away. See below for a side-by-side experience. The left side shows the experience without caching and the right side shows the improved performance with caching.

Read on for more.

Speedata Launches New Workload Analyzer Tool for Spark Queries

Speedata’s Workload Analyzer tool analyzes log files, providing data engineers and platform administrators with valuable insights into the performance of their Spark queries. The tool demonstrates how an enterprise’s workload would perform in different environments, assisting engineers in determining the impact of certain infrastructure decisions such as deploying a faster network or adding more servers.

Read on for more.

Starburst Unveils New Data Lake Analytics Platform at Galaxy Launch Week

This platform announcement also comes on the heels of new connectors between dbt Cloud, Tabular and Starburst Galaxy that allow dbt Cloud and Tabular users to easily build modern open data lake architectures and data pipelines spanning multiple data sources on one central plane. Starburst Galaxy is now generally available and can be implemented in just a few clicks.

Read on for more.

Tableau Unveiled Version 2023.2 with Multi-Row Calculations & Line Patterns

With Tableau Prep, you can now easily perform multi-row calculations that enable the computation of table calculations while preparing your data. With just a few clicks, you can perform calculations like the difference from, percent difference from, and moving calculations without the need for complex calculations or coding. Moreover, the expression editor in Tableau Prep now allows you to write LOOKUP calculations.

Read on for more.

Expert Insights Section

Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Solutions Review Set to Host Qlik for Exclusive Spotlight Webinar on June 29

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics and data integration platform vendor Qlik for an exclusive webinar showcasing a leading embedded analytics platform. The demo will be presented by their customer, PERSUIT.

Read on for more.

Solutions Review Set to Host Alteryx for Exclusive Spotlight Webinar on July 27

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Read on for more.

For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Analytics and Data Science News for the Week of June 23; Updates from Databricks, Qlik, Starburst & More appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Solution Spotlight with Alteryx is entitled: Achieving Meaningful Business Impact with AI, ML & Analytics.

What is a Solutions Spotlight?

Solutions Review’s Solution Spotlights are exclusive webinar events for industry professionals across enterprise technology. Since its first virtual event in June 2020, Solutions Review has expanded its multimedia capabilities in response to the overwhelming demand for these kinds of events. Solutions Review’s current menu of online offerings includes the Demo Day, Solution Spotlight, best practices or case study webinars, and panel discussions. And the best part about the “Spotlight” series? They are free to attend!

Why You Should Attend

Solutions Review is one of the largest communities of IT executives, directors, and decision-makers across enterprise technology marketplaces. Every year over 10 million people come to Solutions Review’s collection of sites for the latest news, best practices, and insights into solving some of their most complex problems.

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics, data science, and automation vendor Alteryx. Through case studies and practical examples, Alteryx’s Field Chief Data & Analytics Officer, Heather Harris, will help you learn the keys to capturing the business impact or your analytic solutions.

Speakers

• Heather Harris, Field Chief Data & Analytics Officer

About Alteryx

Alteryx powers analytics for all with the award-winning Alteryx Analytics Cloud Platform. With Alteryx, enterprises can make intelligent decisions across their organizations with automated, AI-driven insights. More than 8,300 customers globally rely on Alteryx to democratize analytics across use cases and deliver high-impact business outcomes. Alteryx AiDIN is the industry’s first engine that combines the power of AI, machine learning, and generative AI with the Alteryx Analytics Cloud Platform to accelerate analytics efficiency and productivity.

FAQ

• What: Achieving Meaningful Business Impact with AI, ML & Analytics
• When: Thursday, July 27, 2023, at 12:00 PM Eastern Time
• Where: Zoom meeting (see registration page for more detail)

Register for Solutions Review’s Solution Spotlight with Alteryx FREE

The post What to Expect at Solutions Review’s Spotlight with Alteryx on July 27 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Premium Content Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Quantexa Chief Product Officer Dan Higgins offers the secret of augmented AI-driven business value; it’s clean data.

Years of continued global conflict, heightened economic instability, shifting consumer expectations, and accelerate digital transformation have put organizational leaders under magnified pressure to deliver results. But delivering results at speed is getting even harder, and organizations and business leaders alike are under pressure to make faster, more accurate decisions. In a survey conducted by Gartner, 65 percent of respondents noted that they felt they were forced to make significantly more complex decisions today than they were two years ago. Meanwhile, another 53 percent said that they feel more pressure to explain or justify their decision-making – a clear sign of the missing link between rushing to automate and understanding exactly what is being computerized and why.

This is where we often see organizations turn to technologies like artificial intelligence (AI) and machine learning (ML) to support their decision-making. However, the biggest challenge we see is that organizations often implement these technologies without fully considering the importance of context. For AI and ML to make effective predictions and decisions, they need contextual information. It is important to keep in mind that automation challenges stem from more than just complex lines of code; the quality of input data plays a crucial role as well.

Solving A Chicken-And-Egg Problem: Data Quality and AI Business Value

Any application of AI and ML will only be as good as the quality of the data that is inputted. This is why to produce higher-performing AI algorithms, data scientists are laser-focused on working with dependable and transparent data. For example, if you build a classifier to distinguish between photos of cubic zirconia and diamonds, data scientists would ideally like an input image dataset certified by a jeweler. If they couldn’t source this, then the obvious next best place to find this might be online. But this is where challenges of entry error and mislabelling come into play.

There is also the challenge of inconsistent data entry, where a single entity may be referred to using different names. For example, if we were to take my name as an example, I Daniel John Higgins may appear as D.J. Higgins, Dan Higgins, Mr. Higgins, or in a like manner. The same applies to businesses, which may be referred to by their full legal name or a shortened version.

It is crucial for the algorithm to be able to recognize and learn from a variety of different names and formats. This becomes particularly challenging when we consider the sheer scale of data and the number of entities with similar names. The challenge is compounded by the number of individuals and organizations that share the same name. Understanding this scenario and its implications is referred to as context. The algorithm must be able to learn from a full range of different names and formats in order to make these sorts of distinctions.

Unlocking the Power of Your Data to Transform Your Business

Fewer than half (42 percent) of global IT decision-makers trust the accuracy of their organization’s data. This according to new research by Quantexa, which also uncovered that one in eight customer records in the U.S. is a duplicate – meaning that a massive number of organizations cannot differentiate between me as D.J Higgins, Dan Higgins and Daniel John Higgins.

Data is crucial to the success of digital transformation initiatives that use data to enhance operational efficiencies, customer value, and to create new avenues for revenue generation. The truth is, we’ve never had more data. So, despite having the potential to act as an organization’s greatest strength, data can also act as the greatest barrier to its transformation efforts.

Recently, enterprises are projected to have invested a staggering $1.3 trillion (USD) towards digital transformation. Unsurprisingly, a whopping 70 percent of these initiatives have fallen short because companies prioritized other technology investments over the data culture necessary to support their intended objectives.

This challenge will only continue to grow as these efforts continue to snowball generating fivefold more datapoints and adding to the complexity problem across industries.

We then find ourselves in a situation whereby in some industries, such as banking and financial services, organizations may fall victim to this data ‘context gap.’ This is as a direct result of having duplicate datasets relating to the same customer spread across various CRM and other management tools and systems. It can be a simple duplication error, but the impact on insight is instrumental. For example, if the customer’s name is spelled with just one letter out of place on one system but not another, there’s a strong chance that the organization will consider these two entries to be two unique entities, even though they refer to the same person. This is the very nature of siloed data. Without context, deploying any sort of significant analysis is next to impossible, stunting the decision-making process altogether.

To gain a 360-degree view of your customers in a scalable way, it requires more than simply combing through archives to try to spot duplicates manually. Manual data management is not only slow and laborious, but also extremely prone to human error. Legacy methods of doing this, such as Master Data Management (MDM), historically have not been effective in identifying these ‘missing links’ and connecting them to an individual customer. To effectively do this, organizations will need to deploy an emerging category of product that does – entity resolution.

Entity Resolution Brings Rich Context into Focus

By leveraging advanced AI and machine learning models, entity resolution effectively connects, standardizes, and parses data to identify like entities in a cohesive manner. This is achieved by grouping related records, thereby creating a set of attributes, and labeled links for each entity. In contrast to the conventional method of record-to-record matching utilized by MDM systems, entity resolution offers organizations the ability to introduce new entity nodes, which act as a crucial connection point for linking real-world data.

This leads to more accurate and efficient data linking, including the ability to match high-value external data sources such as corporate registry information that was previously difficult to link reliably. Quantexa’s same research shows that only 27% of organizations globally currently use entity resolution technology for mastering their data and making informed decisions.

With widespread duplicate data across various databases and data storage systems, entity resolution is critical for decision intelligence, helping companies avoid making decisions based on inaccurate or incomplete data.

All Roads Lead Back to Clean Data

It’s not a secret just how keenly aware organizations are when it comes to the importance of using data to enhance their decision-making capabilities. In order to break down data silos, companies must parse through a bog of duplicate redundant data, which can have a ripple effect on decision-making efficiency

and accuracy. This leads to wasted resources across data, IT, and business teams and hinders a company’s ability to quickly identify risks and provide top-notch customer service. That is why, ultimately, achieving intelligent decision-making all comes down to the foundation of your data.

The post The Secret to Augmenting AI-Driven Business Value? Clean Data appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Premium Content Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, DoiT International Machine Learning Architect Jared Burns offers a brief on getting the most from machine learning.

In an effort to become more data-driven, companies have been experimenting with machine learning (ML). A subset of artificial intelligence (AI), ML takes in historical data, identifies patterns and outputs new values, enabling applications to quickly predict outcomes without the need for detailed programming.

Unfortunately, many early efforts have failed to fully harness ML due to incomplete plans to action that data, threatening investments and undermining progress. In a survey of corporate executives by NewVantage Partners, a resounding 99% of respondents said their firms had active investments in big data and AI. Yet just 39% said they are managing data as an asset and only 24.4% claimed to have created a data culture within their firms.

The following approaches to ML can help companies develop more effective data strategies and best use ML to drive actionable data insights that will produce valuable output.

Build a Strong Foundation

It was just a few years ago that companies began venturing into ML. However, many rushed in too quickly and did not have the foundational aspects of their ML lifecycle set up properly. With the AI hype in full force, the speed with which companies moved led to major mistakes and insufficient planning.

For instance, even when teams had the right skill sets in place, there would often be engineers working separately on the same data tasks. This could corrupt data, return inconsistent conclusions and waste investments. While a number of companies have found some technical success with ML, the business value was not nearly as strong as expected, and many are looking for a more practical approach to ML technology based on long term, sustainable AI infrastructures.

The first steps to any data analysis strategy are to create a plan and build a strong foundation. Teams need to understand what value they’re looking to extract, how much they can invest and the expected timeframe for results. They should also have ample checkpoints built in to ensure they’re getting accurate, valuable results.

The ability for teams to collaborate effectively is key to success. Strategies to foster this should include:

• Moving workloads to the cloud so they are easily accessible for all collaborators and can scale according to need.
• Using source control or GitHub to track changes and deploy to separate environments for testing and production.
• Adopting a pipeline approach so the whole process as well as the data can be versioned, tracked and cataloged after each step in the process.
• Achieving replicability so that others can rerun a program and produce the same result, determine how it was reached and easily understand any changes to the data.

Make the Most of Your Data

The types of data that companies can extract value from vary, and the ways they process and store the data varies too. These data types can include:

• Unstructured data, such as raw text input that needs to be analyzed for patterns;
• Images companies want to extract insights from; and
• Operational data or tabular data, including any kind of measurements.

Fortunately, with all the technology and resources that Google and AWS have invested in, it’s easy to dive into these areas. In fact, with just a few clicks, a model can be created to analyze and understand data, making it easy for a company with any level of sophistication to begin to parse.

For customers seeking a more customized approach, tools like Python can help build a bespoke ML system from scratch that better aligns with their workflow. Ideally, a company should be able to blend the two pathways, building custom models while enlisting pre-built models to pressure test algorithms for accuracy.

Strive for High Quality

Above all else, the quality of the data and model is paramount – high quality input results in high quality output. Frequent testing is vital to operating a strong ML system and extracting real value.

If you don’t have a high performing data engineering team, you’re not going to develop good models. Data needs to be well cataloged and transformed in a way that’s easy for ML teams to adopt and audit. Monitoring data quality that feeds ML algorithms is critical. All of the features that go into a model need to maintain a similar output in the real world versus what was trained. If the model is delivering output that is different from reality, then either it or the data is flawed.

Along with tracking data and features that go into a model, validating models is also an important aspect of training and using it in a production scenario. Validation refers to the process of confirming the model actually achieves its intended purpose. In most situations, this will involve confirmation that it is predictive under the conditions of its planned use. You may have times when models find unintended consequences if not well tested.

Find a Balance Between Short & Long-Term Value

An approach has to be balanced between short and long term value. Every time you train a model, you need to think about ROI and if it’s worth investing more resources. The goal is always to operationalize a model that can reteach itself on its own and deliver insights that pay for themselves.

Models can be set up to automatically train and deploy based on changes to incoming data, new model architectures, or on a scheduled cadence. The resulting scalability means you can adopt new use cases or drive something more accurate. However, automation is not a substitute for human oversight. Consistent evaluation is essential to understanding if it’s worth the investment and effort of maintaining. Also, model explainability is critical to understanding whether the model’s predictions are causing bias and introducing unintended consequences, which also needs to be incorporated into the ROI of training a model.

Some data streams are very active and require constant evaluation and maintenance. Others are less urgent and data can be processed at less frequent intervals. Determining frequency is a balance of risk (what are the consequences if the AI is making mistakes?), input speed (how often is new data coming in?), and cost/value (how much is an AI operation costing each time it runs and how valuable is the output?).

Pin Down Cloud Costs

Always keep in mind the cloud-cost element and understand each component of that cost (automated or custom).

It’s important to consider both storage of the data and training time/budget of the model, as training the model will burn through compute budget just to retrain. Also, if using an automated ML model, remember that it’s easy to “fall asleep at the wheel” and for costs to mount.

If customizing a model, you need to think about data transformations, training and deployment, as each of these have different volumes of spend. Does a particular use case require an online scenario (constant input/output of data) or a batch scenario (generating predictions nightly/weekly)? You might eventually even scale up to using Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), which can get very expensive. Consider enlisting partners that can help pin down ML spending and control operations to ensure you don’t receive any surprise bills.

Lean on Resources and a Solid Team to Get Started

Blogs from Google and Amazon offer a number of ways to get a quick start. Google, for example, has a quick lab repository of public data to quickly train and deploy a model. Both providers also have training tools that can help get project leaders up to speed.

Next, it’s important to build a strong team of data engineers experienced in ML. Once this is in place, you’ll be able to lean on this unit to build an intuitive model accessible to the wider team. The time frame for getting an ML program started varies based on scale, type and quality of data. Companies well positioned with a smart plan can get one up and running within a matter of weeks.

Tools are available within both Google and Amazon to help explain data and pinpoint issues. This can enable a company to tweak and adjust a model accordingly. But ultimately, the model will only be as good as its data, which will require ongoing care and validation.

The post Lessons Learned: Getting the Most from Machine Learning appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Premium Content Series is a collection of contributed articles written by industry experts in enterprise software categories. In this feature, Alteryx CIO Trevor Schulze offers commentary on driving value and accelerating innovation amidst budget cuts.

Organizations are under increasing financial and competitive pressure, all while needing to keep up with growing customer demands. They are seeking ways to differentiate performance from peers, prioritize key products and services, and position their companies to thrive in today’s competitive market.

Unfortunately, today’s current economic climate has many business and IT leaders navigating budget cuts while trying to avoid critical mistakes that could hamper progress toward business goals. But priorities such as digitization, customer experience, time to market, and increasing revenue remain top of mind for business leaders – regardless of economic cycles.

Companies that demonstrate resilience during times of crisis invest in ways of working that galvanize innovation by expanding – not contracting – access to core technologies. IT leaders can turn adversity into opportunity by making informed decisions about where to cut, maintain, or even increase technology spending. After all, those who wisely invest in their digital capabilities – and data – in a downturn have proven time and time again to come out the other side stronger and pull away from their competition.

Making Long-Term Intentional Decisions

Combining the current economy with increased corporate overhead costs, many business and IT leaders feel forced to implement broad-brush budget cuts, which could mean penalizing both efficient parts and high-performing areas of an organization. This can result in lost value and negatively impact the organization’s ability to remain competitive.

IT leaders who move too quickly with these cuts in an attempt to streamline operations may very well complicate existing processes and introduce potentially harmful risks to the organization. They need to approach these decisions intentionally using an asset they already have: data.

The mission of data and analytics is to manage data resources and create analytic insight that can help organizations make the most informed decisions to better navigate these rocky times. Further, the ability to squeeze every ounce of strategic and operational insight from company data is increasingly essential to increasing profitability and uncovering new revenue streams that will help the company thrive.

IT leaders who can identify sources of complexity and inefficiency and predict the impact of changes will give stakeholders and decision-makers the confidence to maintain spending on resources that deliver tangible business value. For those looking to trim budget without hampering business acceleration, the following strategies are critical to success:

Get Everyone Working With Data

Data and analytics strategies connected to operating models will put business and IT leaders in a position to positively impact operational costs, reduce exposure to business risk, and deliver on the promise of business value. But first, organizations need to build a culture where data is the basis of every decision and strategy and where all employees are on board with this approach and mindset.

You need to ensure everyone across an organization knows how to use data to make better decisions. Make it easy by providing no-code/low-code automation solutions that enable any employee, regardless of their technical skills, to turn data into powerful business insights. This will help your business more easily navigate the unpreditable climate ahead. Furthermore, investing in democratizing data and analytics results in long-term resilience beyond the budget cuts many organizations are faced with today.

Use a Variety of Data Sources & Types

You don’t need to always start from zero each time you have a business problem to solve. In fact, the answers you need to make better decisions are often hiding in plain sight, yet not enough organizations are mining through all of their resources. This is a missed opportunity when you consider that organizations that use a variety of data sources and types are best positioned to get insights that lead to improved customer acquisition, retention, and experience.

Do More With Less

Oftentimes, business units fall victim to “tool sprawl” and run up cloud costs on dozens of disconnected tools that can’t scale across multiple teams – making point solutions easier targets for budget cuts and leaving teams who rely on them in the dust.

Investing in platforms that support multiple personas, unlimited use cases, and feature expansion is key to reducing costs, driving ROI and future proofing investments. Users will also be the first to experiment and take advantage of system updates and new features as they’re introduced, driving faster innovation. Not having to find point solutions for every niche problem you come across will save you from the hassle of onboarding a new vendor every single time, especially considering your platform may be introducing the new capabilities and modules you may very well need.

Case in point: a new study from BCG found that using “a data platform and flexible, scalable technology platforms and applications to facilitate data access and support business needs easily and flexibly” is one of the key attributes of companies best positioned to move into new high-growth markets.

Focus on Tasks that Matter Most

Another key area of investment to discuss with your stakeholders is to enable your team to do more and increase productivity by doubling down on automation.

Why have knowledge workers spend their time doing manual work in spreadsheets, when they can spend their time working on projects that drive top-line growth and bottom-line returns? Meanwhile, automating mundance tasks can free up IT and data teams to work on complex projects that rely on their technical expertise.

Above all, it’s essential for business and IT leaders to start every budgetary decision with data. CIOs and their teams require visibility into their tech spending to ensure efficiency and strategic alignment. Today’s ever-changing economic climate requires data and analytics to help organizations derive insights for significant competitive and operational advantages in customer acquisition, retention, and experience. Those organizations that will thrive beyond the rocky times will be the ones that regularly examine and benchmark investments across multiple cost views to facilitate smarter spending and better business outcomes.

The post Driving Value and Accelerating Business Innovation Amidst Budget Cuts appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review editors curated this list of the most noteworthy analytics and data science news items for the week of June 16, 2023.

Keeping tabs on all the most relevant analytics and data science news can be a time-consuming task. As a result, our editorial team aims to provide a summary of the top headlines from the last week, in this space. Solutions Review editors will curate vendor product news, mergers and acquisitions, venture capital funding, talent acquisition, and other noteworthy analytics and data science news items.

Analytics and Data Science News for the Week of June 16, 2023

Alteryx Announces New Integrations with Databricks

These capabilities build on Alteryx’s portfolio of innovations, which includes Alteryx AiDIN, and Databricks’ large language models (LLM) like the recent Dolly, that make analytics, machine learning, and generative AI more accessible so every user across the enterprise can drive decisions that contribute to positive business outcomes.

Read on for more.

Databricks Publishes Well-Architected Framework for its Lakehouse Platform

In general, well-architected frameworks for cloud services are collections of best practices, design principles, and architectural guidelines that help organizations design, build, and operate reliable, secure, efficient, and cost-effective systems in the cloud. Public cloud providers such as AWS, Microsoft, and Google have such framework documentation.

Read on for more.

Graphtext Nabs $4.6 Million Venture Capital for Exploratory Data Analysis

Led by Victoriano Izquierdo and Miguel Cantón, Graphext is a data exploration and predictive modeling platform that enables data scientists to explore data, create predictive models and collaborate with the business in a single place. The system can connect with, read, and write data from any modern data warehouse.

Read on for more.

Microsoft Releases June 2023 Power BI Feature Summary

This month, Microsoft announced updates to On-Object interaction, a new demo experience to the Power BI embedded playground, which simplifies the process of exploring embedding Power BI in your application, creating Power BI reports instantly with Jupyter Notebooks, and Power BI Desktop Developer mode.

Read on for more.

Salesforce Unveils New AI Cloud for CRM

AI Cloud is a suite of capabilities optimized for delivering trusted, open, and real-time generative experiences across all applications and workflows. AI Cloud’s new Einstein GPT Trust Layer resolves concerns of risks associated with adopting generative AI by enabling customers to meet their enterprise data security and compliance demands, while offering customers the benefits of generative AI.

Read on for more.

Expert Insights Section

Watch this space each week as Solutions Review editors will use it to share new Expert Insights Series articles, Contributed Shorts videos, Expert Roundtable and event replays, and other curated content to help you gain a forward-thinking analysis and remain on-trend. All to meet the demand for what its editors do best: bring industry experts together to publish the web’s leading insights for enterprise technology practitioners.

Solutions Review Set to Host Qlik for Exclusive Spotlight Webinar on June 29

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics and data integration platform vendor Qlik for an exclusive webinar showcasing a leading embedded analytics platform. The demo will be presented by their customer, PERSUIT.

Read on for more.

For consideration in future data science news roundups, send your announcements to the editor: tking@solutionsreview.com.

The post Analytics and Data Science News for the Week of June 16; Updates from Databricks, Power BI, Qlik & More appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .

Solutions Review’s Solution Spotlight with Qlik is entitled: The Future is Now with Embedded Analytics: Qlik’s End-to-End Platform.

What is a Solutions Spotlight?

Solutions Review’s Solution Spotlights are exclusive webinar events for industry professionals across enterprise technology. Since its first virtual event in June 2020, Solutions Review has expanded its multimedia capabilities in response to the overwhelming demand for these kinds of events. Solutions Review’s current menu of online offerings includes the Demo Day, Solution Spotlight, best practices or case study webinars, and panel discussions. And the best part about the “Spotlight” series? They are free to attend!

Why You Should Attend

Solutions Review is one of the largest communities of IT executives, directors, and decision-makers across enterprise technology marketplaces. Every year over 10 million people come to Solutions Review’s collection of sites for the latest news, best practices, and insights into solving some of their most complex problems.

With the next Solutions Spotlight event, the team at Solutions Review has partnered with leading analytics and data integration platform vendor Qlik for an exclusive webinar showcasing a leading embedded analytics platform. The demo will be presented by their customer PERSUIT.

Speakers

• Catherine Frye, Director, OEM Product Marketing

About Qlik

Qlik Sense is one of the most widely used business intelligence and data analytics platforms in the world. Qlik enables organizations to combine all their data sources into a single view. It also allows users to develop, extend and embed visual analytics in existing applications and portals. Embedded functionality is done within a common governance and security framework. Users can build and embed Qlik as simple mashups or integrate within applications, information services or IoT platforms.

FAQ

• What: The Future is Now with Embedded Analytics: Qlik’s End-to-End Platform
• When: Thursday, June 29, 2023, at 12:00 PM Eastern Time
• Where: Zoom meeting (see registration page for more detail)

Register for Solutions Review’s Solution Spotlight with Qlik FREE

The post What to Expect at Solutions Review’s Solution Spotlight with Qlik on June 29 appeared first on Best Business Intelligence and Data Analytics Tools, Software, Solutions & Vendors .