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What Google Docs did for word processing and Figma did for interface design, Hex hopes to do for data science. Which is to say, make data science and analytics a collaborative process, using a slick web-based user interface.

I spoke to Hex CEO Barry McCardel about how the data stack will change over the 2020s and what it means for data science notebooks, a common tool for data scientists.

We think of ourselves as building the frontend for the modern data stack, McCardel began. While thats the ultimate goal, practically speaking Hex fits into a category of tools known as the data science notebook. It competes with other such products, like Jupyter, Amazon SageMaker and Google Colab. Programming notebooks have actually existed since at least the late 1980s when Mathematica was launched. But in the modern cloud computing era, the open source Jupyter Notebook has been the flag-bearer for the data science notebook industry. Project Jupyter was launched in 2014, as a spinoff of IPython (Interactive Python).

I think at the core, notebooks are really just a very nice way to be able to do iterative analysis, McCardel told me. It basically breaks your code up into chunks, called cells. So you have re-runnable individual chunks. And those chunks can both run as a small unit, but also show you the output. So that was really one of the core innovations with the notebook format, where I can run a small bunch of the code and see what it does maybe Im seeing a chart, or a table, or just a result set, or whatever.

However, McCardel views Hex as more than just a notebook, or a Jupyter in the cloud (which is how he classifies some of Hexs competitors). What we see from our customers is that they arent just looking for a notebook solution, he said. Theyre looking for something that helps them share and helps them bring in people from different types of backgrounds and stakeholders.

Hex is trying to position itself as not just a tool for data scientists, but for data analysts and Business Intelligence (BI) roles both of which are less technical and more business-focused.

In Hex, users can use a no-code interface, or do queries in SQL or Python. Data scientists have traditionally used Python, but according to McCardel, that isnt necessarily the case with Hex users.

This idea that, oh, data scientists are high-end technical and use Python, and SQL is lesser, is not true at all! SQL is really good at a bunch of things. And youll talk to a lot of data scientists who use mostly SQL, and thats totally cool. It doesnt make them less of a data scientist. Theres a lot of really great things you can do [with SQL].

Clearly, though, Hex is eyeing a much broader market than competing tools that simply focus on data scientists. Its reminiscent of many of the popular low-code platforms that have emerged in the enterprise development market in recent years, most of which also target business users.

We want it to be easily accessible for people of all technicality levels, said McCardel, regarding the target users for Hex. People with just baseline data knowledge and curiosity, people who might be coming from spreadsheets or BI tools.

So are Python users adopting Hex too, I asked?

Yeah, its everything Jupyter can do and more. If youre coming from Python-based data science work, Hex will be familiar and powerful and give you a lot of new superpowers.

Another interesting aspect of Hex is that it has seemingly joined forces with two other modern data companies. Snowflake and Databricks, two leading data warehouses, were both investors in Hexs most recent funding round in March. So I asked McCardel, how would a data professional use Hex alongside one or both of those other tools?

So, Hex sits on top of those environments on top of Snowflake and on top of Databricks and helps customers make the most of the data that are in those environments. If you have brought all of your data into a data warehouse, including both of those two, often the next question is: so what now? How do I make this useful and impactful for the organization? Hex really seeks to answer that question.

Ultimately though, a lot hinges on Hexs ability to emulate the likes of Google Docs and Figma in becoming a user-friendly tool that also offers enough oomph to keep power users happy. In this case, the data scientists are the power users.

McCardel admires how Figma allows designers to share their work and bring other people into the process as stakeholders, and he wants to achieve that in Hex for data scientists. Not only that, he said, but Figma translates a lot of those people [stakeholders] into editors. The users become creators in the system, not just viewers.

So I get really excited when I see that type of thing happen in Hex, he continued. We see engineers and product managers and other people come in and actually become editors. Hex is not just for high-end data scientists.

Finally, we talked about where things are headed for the data stack. With tools like Snowflake, DataBricks, and perhaps now Hex, the tools available to enterprise users are increasingly sophisticated and easily accessible via the web. I was curious for McCardels thoughts on what comes next.

The last few years have been this revolution in the data world, on the integration story, he said. Im using Fivetran or Stitch or Airbyte to get my data from source into my warehouse. I now have my data in my warehouse, I can use DBT [data build tool] to transform it. I can use observability tools to monitor for quality. [] I can store this at any scale. I can run queries at any scale, you dont need to worry about provisioning servers anymore. I have been working in data for like 10 years, and its such a huge difference from 10 years ago. Its like the ability as an organization to just have all of my data in one place and have it integrated, clean and ready to go.

However, he added, the story is not over. He thinks the next part of the cloud data revolution will be in frontend tooling, or as he put it, what that new frontend for the data stack is.

Hex is clearly eyeing the data stack frontend, but well just have to wait and see whether it can capture that market as well as Figma did for interface design.

Feature image via Shutterstock.

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Hex Wants to Build the Frontend for the Modern Data Stack - thenewstack.io

Hays Martin Pardey explains what a data analytics professional does, what they can expect in their career and how to develop the necessary skills.

Data analytics has become a critical part of many businesses, but even within the analytics space, there are many roles available, including a data analyst, data engineer, data scientist and data manager. All these roles contribute to the goal of deriving meaningful insight from data.

Data analysts derive insight from data, while data engineers extract and manipulate data from systems and build data capability.

Data scientists, meanwhile, are able to build predictive models that help organisations make decisions based on potential future events, as well as driving automation and artificial intelligence systems. Lastly, data managers look after the data to ensure quality, governance and security.

Data roles used to be mainly centred around extracting simple management information and building reports for key stakeholders so that they could accurately analyse company performance. Now, as organisations become more data-centric, these roles have become complex.

There is a greater focus on ensuring that vast amounts of data can be analysed and accessed at any time across the organisation, as well as be used to build predictive models and power AI systems.

As a result, many organisations have now built internal data practices that employ many different types of data professional.

We have seen salaries for these roles increase steadily over the last few years as demand grows among organisations for better data.

An entry-level data analyst in permanent employment can expect to earn anywhere from 25,000 to 30,000 in the UK, while advanced data engineers and data scientists could even command six-figure salaries. Contract rates vary greatly depending on roles and skills.

Firstly, decide what area of data you want to work in. Are you highly analytical? Do you enjoy number-crunching and solving business problems? Or are you more technical, with a thirst for building data platforms and extracting the right data?

Where possible, try to develop your skills. See whether you can get any hands-on experience where you can actually apply the skills although, admittedly, this is much easier if you are already at an organisation or educational institution.

If youre in employment, look for opportunities to work with data within your current company. Ive heard of companies that are training employees in non-data roles to become data professionals in internal data academies.

For those in higher education, enquire about any projects you can work on. Many universities now have partnerships with major corporate organisations so that you can contribute to real life data projects.

While the data and analytics profession is strong, the need for data insights across all business levels means data skills have become critical even outside the technical sphere.

Research from Digital Realty revealed that more than one in five (21pc) IT leaders globally highlighted that the lack of internal talent to analyse data, and the lack of talent to build technical capacity (21pc), are among the greatest obstacles their organisations are facing when drawing insights from their data.

Luckily, there is a whole host of online courses out there, depending on what area of data you wish to pursue. For example, My Learning, Hays free online learning portal, has lessons in data science and analytics for those interested.

Data analysis forms part of a lot of roles these days. If youre in employment, you will likely have access to data and reporting systems in your current role. Make sure that you are fully trained on how to use them.

Seek out the head of data in your current company and talk to them about what it takes and whether they can provide you with any support. Enquire about learning resources that your employer already provides and whether there are any courses, classes or even modules that are directly relevant to what you want.

The value of being able to work with data effectively is high, so organisations are likely to see the benefit in supporting you in upskilling as they will benefit from your new skillset at the same time.

By Martin Pardey

Martin Pardey is a director for technology solutions at Hays UK with more than 20 years personal recruitment experience in the sector.

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These are the roles available in data and analytics - Siliconrepublic.com

Arijit Sengupta once wrote an entire book titled Why A.I. is A Waste of Money. Thats a counterintuitive title for a guy who makes his money selling A.I. software to big companies. But Sengupta didnt mean it ironically. He knows firsthand that for too many companies, A.I. doesnt deliver the financial returns company officials expect. Thats borne out in a slew of recent surveys, where business leaders have put the failure rate of A.I. projects at between 83% and 92%. As an industry, were worse than gambling in terms of producing financial returns, Sengupta says.

Sengupta has a background in computer science but he also has an MBA. He founded BeyondCore, a data analytics software company that Salesforce acquired in 2016 for a reported $110 million. Now hes started Aible, a San Francisco-based company that provides software that makes it easier for companies to run A.I. algorithms on their data and build A.I. systems that deliver business value.

Aible makes an unusual pledge in the A.I. industry: it promises customers will see positive business impact in 30 days, or they dont have to pay. Their website is chock full of case studies. The key, Sengupta says, is figuring out what data the company has available and what it can do easily with that data. If you just say what do you want, people ask for the flying car from Back to the Future, he says. We explore the data and tell them what is realistic and what options they have.

One reason most A.I. projects fail, as Sengupta sees it, is that data scientists and machine learning engineers are taught to look at model performance (how well does a given algorithm do with a given data set at making a prediction) instead of business performance (how much money, in either additional revenue or cost-savings, can applying A.I. to a given dataset generate).

To illustrate this point, Aible has run a challenge in conjunction with UC Berkeley: it pits university-level data science students against high school 10th graders using a real-world data set comprised of 56,000 anonymized patients from a major hospital. The competing teams must find the algorithm for discharging patients from the 400-bed hospital that will make the hospital the most money, understanding that keeping patients in the hospital unnecessarily adds costs, but so does making a mistake that sees the same patient later readmitted. The winner gets $5,000. The data scientists can use any data science software tools they want, while the high school kids use Aibles software. The high school kids have beaten the data scientistsby a mileevery time theyve run the competition, Sengupta says.

The teens, Sengupta says, are able to keep their eyes on the bottom line. Theyre not concerned with the particular model that Aible suggests (Aible works by training hundreds of different models and finding the one that works best for a given business goal), whereas the data scientists get caught up on training fancy algorithms and maximizing accurate discharge predictions, but losing sight of dollars and cents.

Senguptas point is that ignoring, or not actually understanding, the business use of an A.I. system can be downright dangerous. He describes what he calls the A.I. death spiral, where an A.I. system maximizes the wrong outcome and literally runs a business into the ground. Take for example an A.I. system designed to predict which sales prospects are most likely to convert to paying customers. The system can achieve a higher accuracy score by being conservativeonly identifying prospects that are highly likely to convert. But that shrinks the pool of possible customers significantly. If you keep running this optimization process using only the small number of customers who convert, the pool will just keep shrinking, until eventually the business winds up with too few customers to sustain itself. Customer win rate, Sengupta says, is the wrong metricthe A.I. should be trained to optimize revenue or profits, or maybe overall customer growth, not conversion rates.

Sidestepping these pitfalls requires a little bit of machine learning understanding, but a lot of business understanding. Sengupta is not alone in hammering home this theme. Its a point that a lot of those working on A.I. in commercial settingsincluding deep learning pioneers such as Andrew Ngare increasingly making: algorithms and computing power are, for the most part, becoming commodities. In most of the case studies on Aibles website, customers used the startups cloud-based software to train hundreds of different models, sometimes in less than 10 minutes of computing time. Then the business picks the model that works best.

What differentiates businesses in their use of A.I. is what data they have, how they curate it, and exactly what they ask the A.I. system to do. Building models is becoming a commodity, Sengupta says. But extracting value from the model is not trivial, thats not a commodity.

With that, heres the rest of this weeks A.I. news.

Jeremy Kahn@jeremyakahnjeremy.kahn@fortune.com

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Want your companys A.I. project to succeed? Dont hand it to the data scientists, says this CEO - Fortune

Ocient, a leading hyperscale data analytics solutions company serving organizations that derive value from analyzing trillions of data records in interactive time,released version 20 of the Ocient Hyperscale Data Warehouse. New features and optimizations include a suite of indexes, native complex data types, and the creation of data pipelines at scale to enable faster and more secure analysis of log and network data for multi-petabyte workloads. Customers in telecommunications, government, and operational IT can now use Ocient to find needle-in-the-haystack insights across a broad set of uses cases including call detail record (CDR) search, IP data record (IPDR) search, Internet connection record (ICR) search, and content delivery network (CDN) optimization.

Ocient introduced a new suite of indexes to further enhance the cost-effective performance at scale delivered by its Compute Adjacent Storage Architecture. Ocients new suite of indexes includes N-gram indexes to accelerate searching text data such as URLs and log messages for hyperscale log and network analysis. With up to 40 times performance gains on these workloads, network analysts can work with multi-petabyte datasets faster to find and diagnose issues and predict future issues or outages within hyperscale distributed networks while cutting systems and operational costs by up to 80%.

Industries such as telecommunications, government, and operational IT generate massive multi-petabyte log data that must be analyzed in real time for performance monitoring, business insights, and compliance with industry regulations. The volume of data combined with the requirement to instantly ingest, perform analytics, and deliver insights on that data is challenging legacy systems and creating a new class of hyperscale analytics systems purpose-built to meet such stringent requirements, said David Menninger, SVP and research director, Ventana Research.

Ocients version 20 release includes additional features to enhance performance, streamline data integration, create data pipelines at scale, and consolidate data movement for improved security. These features include:

The Ocient Hyperscale Data Warehouse version 20 offers significant enhancements to better support our customers requirements for hyperscale log and network metadata analysis, said Chris Gladwin, co-founder and CEO, Ocient. We see many existing systems unable to handle the sheer volume of data our customers are dealing with, and this release provides our users with the tools they need to rapidly gain new insights, comply with regulations and transform their businesses.

The Ocient Hyperscale Data Warehouse can be deployed as a managed service on-premises, in Google Cloud procured through Google Cloud Marketplace, in AWS, or in the OcientCloud.

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Ocient Releases the Ocient Hyperscale Data Warehouse version 20 to Optimize Log and Network Metadata Analysis - insideBIGDATA

The size of the worldwide data analytics market was estimated to be USD 34.56 billion in 2022, and it is anticipated to increase between 2022 to 2028. The introduction of machine learning and artificial intelligence (AI) to provide individualised consumer experiences, the increased acceptance of social networking platforms, and the popularity of online shopping are the main factors propelling the data analytics markets expansion.

In response to the COVID-19 epidemic, many businesses have implemented advanced analytics and AI technologies to manage enormously complicated supply chains and engage customers online. Additionally, the epidemic has increased the usage of cutting-edge technologies in many other industries, including data mining, artificial neural networks, and semantic analysis.

The amount of data produced by enterprises globally has increased exponentially in recent years. The acquired data provides insights that help various firms make better, timely, and fact-based decisions. Particularly as it relates to data management and strategic decision-making, this has increased demand for advanced analytics solutions.

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Furthermore, advancements in the big data space have aided in enhancing the evaluation skills of data science experts. Enterprises can improve crucial business processes, goals, and activities by utilising big data analytics. By transforming information into intelligence, firms may meet stakeholder requests, manage data volumes, manage risks, enhance process controls, and increase administrative performance.

On-premise installations give businesses more freedom and control over how to tailor their IT infrastructure, while also decreasing their reliance on the internet and safeguarding sensitive company information from theft and fraud. It is projected that these advantages will persuade major enterprises to choose on-premise deployment.

In addition, businesses in the BFSI industry favour the on-premise model due to increased worries about frauds such as new account fraud and account takeovers. On-premise businesses are more resistant to these scams, which is good news for the segments expansion.

The data analytics market is anticipated to expand as a result of the increasing use of advanced analytics tools for applications including predicting and forecasting electricity consumption, the trade market, and traffic trend predictions. Utilising sophisticated analytics in demand forecasting can support businesses in making profitable decisions. Governmental organisations and other sectors, including banking, manufacturing, and professional services, have recently made significant investments in data analytics.

For instance, to make their data sets informative and maintain their competitiveness in the market, international banks are optimising information, such as the data gathered from social media feeds, customer transactions, and service inquiries, to create data-driven Business-Intelligence (BI) models and implement advanced predictive analytics.

In 2021, the data analytics market segment held a market share of over 35%. The segments expansion can be ascribed to the rising use of social media sites and the rise in virtual businesses that generate significant amounts of data. Additionally, the development of SaaS-based big data analytics has made automation installation simpler and permitted the creation of powerful analytical models using a self-service paradigm. Big data service providers have been urged to enhance their investments in cloud technology in order to acquire a competitive edge by the increased demand for big data analytics solutions.

Regional Analysis:

Over the global advanced market, North America held a significant market share. This is due to the availability of infrastructure that supports the use of cutting-edge analytics and the rise in the usage of cutting-edge technologies like AI and machine learning.

Over the course of the projected period, the Asia Pacific market.The regional market is expanding as a result of big data analytics tools and solutions being widely used there. Additionally, a number of businesses in the area are making significant investments in customer analytics to boost productivity and efficiency. Additionally, regional travel agencies including China Ways LLC, TNT Korea Travel, and Trafalgar are implementing analytical tools for uses like monitoring bus schedules, railway schedules, train breakdowns, and traffic management.

The retail industrys customers rising demand for an omnichannel experience has fueled the segments rise. Well-known businesses like Amazon and Walmart have been effective in leveraging the advantages of various social media sites like Facebook and YouTube. The segment is expected to grow as a result of more retail businesses focusing on providing omnichannel services to their customers.

Competitive Analysis:

The goal of the partnership is to make it possible for users to quickly build and apply models using edge streaming data. Users of Hiv Cell would be able to employ models created with the RapidMiner platform through the integration to enable AI-optimised decision-making wherever necessary. Leading companies in the global market for advanced analytics include: Altair Engineering, The Fair Isaac Company (FICO), Corporation of International Business, Machines, KNIME, Windows Corporation, Microsoft Corporation, Inc., RapidMiner, SAP SE, Inc., SAS Institute, Trianz.

Important Features of the Data Analytics Software Market report: Potential and niche segments/regions exhibiting promising growth. Detailed overview of Data Analytics Software Market. Changing market dynamics of the industry. In-depth Data Analytics Software Market segmentation by Type, Application, etc. Historical, current, and projected market size in terms of volume and value. Recent industry trends and developments. Competitive landscape of Data Analytics Software Market. Strategies of key players and product offerings.

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Table of Contents:1. Data Analytics Software Market Overview2. Impact on Data Analytics Software Market Industry3. Data Analytics Software Market Competition4. Data Analytics Software Market Production, Revenue by Region5. Data Analytics Software Market Supply, Consumption, Export and Import by Region6. Data Analytics Software Market Production, Revenue, Price Trend by Type7. Data Analytics Software Market Analysis by Application8. Data Analytics Software Market Manufacturing Cost Analysis9. Internal Chain, Sourcing Strategy and Downstream Buyers10. Marketing Strategy Analysis, Distributors/Traders11. Market Effect Factors Analysis12. Data Analytics Software Market Forecast (2022-2028)13. Appendix

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Data Analytics Software Market : An Exclusive Study On Upcoming Trends And Growth Opportunities from 2022-2028 | Alteryx, Apache Hadoop, Apache Spark...