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The game of chess in the APC – Businessday

The rupture in the relationship between President Muhammadu Buhari and Bola Tinubu has finally happened. Now, we can predict with reasonable certainty that Buhari does not fancy Tinubu as the presidential flag bearer of the ruling All Progressives Congress (APC).

For upwards of seven years, these two have been forced into an alliance dictated by their mutual interests. It was rumoured that there was an agreement by the duo to take turns at the presidency.

Mr Tinubu kept his own side of the bargain by sticking with Buhari through thick and thin. In 2014, his support was critical to Mr Buhari picking the presidential ticket of the APC. Even more daunting, Tinubu ensured Buharis victory at the polls in the entirety of the Southwest.

Mr Tinubu is not unaware of these realities. There are credible rumours hes already propping a political party that has adopted the name of a hitherto winning political party to serve as his fall-back option in the event he is denied the APC ticket

In 2019, even in the face of poor performance and nothing to show for his four-year leadership, Tinubu still supported him, resorting to ethnic politics and voter intimidation to deliver Lagos and some southwestern states to Buhari.

Even in October 2020, when Mr Buhari sent in troops to massacre innocent youth protesting police brutality at the Lekki toll gates, Tinubu stood behind him at the risk of losing his grip on the voting population of Lagos. Instead of speaking against the killings, Mr Tinubu upbraided the youth and asked them what they were doing at the toll gate at the time.

However, when it was time for Mr Buhari to repay the favour to Tinubu, he has become non-committal and appears to be backing other candidate(s) for the position. Long before the election season, there were rumours that Mr Buharis close circle are not enamoured by Tinubu and wanted another candidate to fly the party ticket at the election. That was why Tinubu pre-empted them all and declared his intention first with the hope that his declaration may weaken the resolve of some of his opponents within the party and force the hand of the President.

That hasnt worked out as planned. Many other contestants also close to President Buhari have since declared their intentions to contest for the partys ticket. More jarring was the declaration of the Vice President, Tinubus political godson. It was said Osinbajo sought and got the permission of the President to throw his hat into the ring. Media reports also suggest a former president, Goodluck Jonathan, is under immense pressure from Mr. Buharis circle to join the race.

Read also:Presidency: Osinbajo, Tinubu in fight to the finish

Although Jonathan has said he will only join the race on the condition that he is endorsed and supported wholeheartedly by Mr Buhari, a tweet by Jonathans former spokesperson that Jonathan is on the verge of joining the presidential race on the platform of the ruling APC has further complicated matters and left analysts guessing as to who exactly will get the presidents endorsement for the polls.

All these have left Tinubu feeling used and betrayed. And for the first time in seven years, he has publicly disassociated himself from the President and offered his first open rebuke and criticism of the Buhari administration.

Speaking a fortnight ago at a youth rally, he chided the Buhari administration for failing to tackle the urgent challenges of the country, offering himself as a better alternative. We feel your anger when you are angry. I dont blame you. The promises of the past have failed to realise, he told an enthusiastic crowd of youth in Lagos who gathered to support his campaign.

We cannot continue with excuses or NEPA failure. No. No nation can make rapid development without electricity. Give us that and if we cannot be successful, then you can abuse us. But you cannot give us erratic electricity that is undependable and then blame us again that we are lazy, Mr. Tinubu told his cheering supporters.

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KCB chess team bullish ahead of East Africa Open Championship – The Star, Kenya

KCB Chess Club will participate in the upcoming East Africa Open Tournament to be played from April 30 toMay 3, in Mombasa.

The regional tournament is an elite annual contest played by the highest-ranked East African players and usually attracts over 200 players competing in the open, ladies and juniors sections.

This year's tournament has attracted over 250 players from Kenya, Uganda, Tanzania and South Sudan and where a prize purse of Sh300,000 awaits. However, junior players will win trophies and medals.

Organised by the Kenya Chess Federation and the Lighthouse Chess Club, Kenya will be represented by players from various clubs including KCB, Nakuru, Lighthouse, Nairobi, Mombasa among others.

The 2021 national champion Martin Njoroge exhibited confidence the event will provide a good opportunity for Kenyan players to gauge themselves against highly-rated players from across the region.

I am looking forward to the tournament and I'm buoyant about a good performance. I analysed my previous matches with my teammate Philip Singe and polished up my weak points to prepare for the busy calendar, Njoroge said.

The tournament will also serve as preparation for the National Chess League, which enters Round5 next month.

It will also provide a good launchpad for the zonal competition, which will be held in Addis Ababa, Ethiopia between May 1 and 10.

Njoroge said he will have to cut short his participation in the East African competition to attend the zonal tournament. The zonal tournament is played by champions of the various leagues in different countries.

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Baseten Gives Data Science and Machine Learning Teams the Superpowers They Need to Build Production-Grade Machine Learning-Powered Apps -…

Baseten formally launched with its product that makes going from machine learning model to production-grade applications fast and easy by giving data science and machine learning teams the ability to incorporate machine learning into business processes without backend, frontend or MLOps knowledge. The product has been in private beta since last summer with well-known brands that have used it for everything from abuse detection to fraud prevention.It is in public beta at this time.

Its clear that the performance and capabilities of machine learning models are no longer the limiting factor to widespread machine learning adoption instead, practitioners are struggling to integrate their models with real world business processes because of the enormous engineering effort required to do so. With Baseten, were reducing this burden and accelerating time to value by productizing the various skills needed to bring models to the real world, said Tuhin Srivastava, co-founder and CEO of Baseten.

Over the last decade, theres been enormous progress in advancing the capabilities of machine learning, driven primarily by new model architectures and the ever-decreasing cost of compute. But the critical step of integrating models with real-world business processes is still a lengthy, expensive process that prevents the majority of businesses from seeing a return on machine learning investments. While a typical machine learning model may take just a few weeks to train, building the infrastructure, APIs and UI so that the model can be used by businesses can take more than six months and requires additional resources in the form of MLOps, backend and frontend engineers.

This is a problem that Basetens co-founders Tuhin Srivastava (CEO), Amir Haghighat (CTO) and Philip Howes (Chief Scientist) encountered first hand at Gumroad. There Haghighat was the head of engineering and Srivastava and Howes were both data scientists who had to learn to become full-stack engineers so they could use machine learning to detect fraud and moderate content. The systems they built at Gumroad are still in use and have screened hundreds of millions of dollars of transactions to date.

The trio founded Baseten so that data scientists dont have to learn to become full-stack engineers in order to build web applications for their machine learning models. Baseten lowers the barrier to usable machine learning by enabling data science and machine learning teams to incorporate their machine learning models into production-grade applications within hours instead of months. With Baseten, data science and machine learning teams can easily serve their models, build backends and frontends and ship applications that solve critical business problems including operations optimization, content moderation, fraud detection and lead scoring.

Customers on Baseten:

Analysts on Baseten:

Baseten Raises $20 Million in Seed and Series A Funding

Baseten also announced that it has raised $8 million in seed funding co-led by Greylock and South Park Commons Fund and $12 million in Series A funding led by Greylock. Baseten is using the funding to expand its engineering and go-to-market teams.

Greylock General Partner and Baseten Board Member Sarah Guo said: Despite the broad understanding that AI has the capability to revolutionize business, most organizations struggle to drive real ROI from theirmachine learning efforts, stymied by the high upfront investment required. Baseten radically reduces the time, specialized expertise, cost and cross-team coordination required to successfully ship machine learning apps to production. Its end-to-end platform frees data science and machine learning teams from grunt work and empowers them to spend more time innovating and iterating to maximize impact. The Baseten team has experienced this pain first-hand, and that authenticity and care shows in the solution theyve designed. Were thrilled to partner with them to democratize access to the revolution in machine learning.

Other participants in the seed round include AI Fund, Caffeinated Capital and angel investors Lachy Groom (ex-Stripe), Greg Brockman (co-founder and CTO of OpenAI), Dylan Field (co-founder and CEO of Figma), Mustafa Suleyman (co-founder of DeepMind) and DJ Patil (ex-Chief Data Scientist of the United States Office of Science and Technology Policy).

Other participants in the A round include South Park Commons and angel investors Lachy Groom, Cristina Cordova (ex-Stripe), Dev Ittycheria (CEO of MongoDB), Jay Simon (ex-President of Atlassian) and Jean-Denis Greze (CTO of Plaid).

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Mperativ Adds New Vice President of Applied Data Science, Machine Learning and AI to Advance Vision for AI in Revenue Marketing – AiThority

Mperativ, the Revenue Marketing Platform that aligns marketing with sales, customer success, and finance on the cause and effect relationships between marketing activities and revenue outcomes, announced the appointment of Nohyun Myungas Vice President of Applied Data Science, Machine Learning and AI. In this new role, Nohyun will lead the development of new Mperativ platform capabilities to help marketers realize the value of AI predictions and seamlessly connect data across the customer journey without having to build a data science practice.

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Throughout my career Ive become acutely familiar with the immense challenges that go-to-market teams face when trying to get a comprehensive and accurate picture of the customer journey

Nohyun has unique and important experience in data science, analytics and AI that will be critical to the growth of the Mperativ Data Science and AI practices, said Jim McHugh, CEO and co-founder of Mperativ. He not only brings the knowledge and skill set to help accelerate the evolution of the Mperativ platform, but his involvement in the technical side of sales organizations will give us a unique perspective on how AI and forecasting can be used to help address the challenges go-to-market teams face.

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Nohyun brings over 20 years of experience as a data and analytics practitioner. Prior to Mperativ he built and scaled high-functioning, multi-disciplinary teams in his roles as Vice President of Global Solution Engineering & Customer Success at OmniSci and as Vice President of Global Solution Engineering at Kinetica. He has worked closely with industry leaders across Telco, Utilities, Automotive and Government verticals to deliver enterprise-grade AI and advanced analytics capabilities to their data practices, pioneering work across autonomous vehicle deployments to telecommunications network optimization and uncovering anomalies from object-detected features of satellite imagery. Nohyuns prior experience has led to the advancement of enterprise-class AI capabilities spanning Autonomous Vehicles, automating Object Detection from optical imagery and Global-Scale Smart Infrastructure initiatives across various industries.

Throughout my career Ive become acutely familiar with the immense challenges that go-to-market teams face when trying to get a comprehensive and accurate picture of the customer journey, said Nohyun. As the world sprints towards becoming more prescriptive and predictive, having operational tools and platforms that can augment business without having to build it in-house will become essential across B2B organizations. I look forward to working with the talented team at Mperativ to bring the true value of AI to marketing leaders so they can better execute engagement strategies that produce their desired revenue outcomes.

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Data Scientist Resume: Template, and Best Tips for Writing One – Dice Insights

Even though 95 percent of employers say thatdata science and analytics skills are hard to find,candidates looking for a data scientist job still need to demonstrate their proficiency in core concepts and skills during the job-search process. A data scientist resume is a key element in telling this story.

A good data scientist resume should include awide range of skillsandqualitiesbeyond the fundamentals of data science. Data is intricate, and most likely, your account managers or creative teams wont understand the technical complexities as you do, explained Lauren Hamer, certified professional rsum writer and founder of LaunchPoint Rsum. Thats why hiring managers look for a mix of technical aptitude and communication skills (e.g., can you work with unstructured data, uncover ways to solve business problems, and present your findings to other stakeholders?), when reviewing data scientist resumes.

In this guide, well cover the major steps to creating an effective data scientist resume, as well as delve into some of the things hiring managers look for in data scientists.

How can a data scientist go about showcasing their technicalandcommunication skills in their resume? Usually this is best done within the work history section, Hamer noted.

In addition to presenting examples of your projects and work, the bullet points should mention the teams and stakeholders you worked with, as well as the medium you used to present the findings to others (detailed Excel reports, case studies, Agile project formats, Zoom, kick-off calls, presentation decks, etc.).

For example:

Produced value-added deliverables for R&D and product marketing by mining and analyzing third-party and customer sentiment data to derive significant, tangible, actionable insights.

Collaborated with data engineers and marketing team to implement ETL process, wrote and optimized SQL queries to perform data extraction to fit the analytical requirements.

Designed rich data visualizations and views using combo charts, stacked bar charts, pareto charts, donut charts, geographic map, transforming the data with Tableau and Matplotlib.

Delivered and communicated research results, recommendations, opportunities to non-technical managers and executive teams, via slide show over Zoom.

To further affirm the skill level of a data scientist and their ability to do their job, Hamer sometimes includes a quote or testimonial from a stakeholder or customer. For example:

Laurens ability to see the bigger picture among the weeds helped our team launch a new product in just 3 months-something that wouldnt have been possible without her detailed algorithms and statistical models. -Bob Jones, senior R&D manager, ABC Company

The other most vital sections to include in a data science resume are technical skills and special projects.

Hamer recommends placing your technical skills summary or toolbox in the top-one third of your rsum, right below your profile summary and before your work experience summary. It should list the platforms, programs, and languages youre familiar with. (Bonus points for prioritizing the specific skills highlighted in the job description.)

While you should briefly mention projects in your work history bullets, Hamer explained, they are so important that they deserve their own section labeled special projects or related projects and a more detailed summary that will help draw the readers eye to them. Again, project summaries provide the perfect platform for explaining your approach, mindset, business acumen and the things that separate great data scientists from good ones.

With that in mind, here is a format for describing each project:

An opening summary should include the target job title in the first line, then convey the candidates best value statement or differentiator something unique.

Think back on feedback youve received from mentors, professors, supervisors, and so on. What do they love about you? Do you understand the big picture? Are you their most reliable worker they dont have to worry about? Are you the one most trusted to train new employees? Mention those in your opening summary:

Goal-oriented Data Scientist offering a proven track record of understanding the business underpinnings of a problem, conducting effective analyses and delivering data-driven insights and value that improve decision making and positively impact the top and bottom lines. Highly motivated and insightful professional with exceptional intrapersonal and communication skills and over six years experience in data mining, extraction, analysis, statistical modeling, machine learning and data visualization. Turning data into valuable information is my passion and forte.

The length of your resume doesnt matter quite as much as the content. Also

Match the job description: To capture the attention of automated and human reviewers, make simple modifications/customizations to match the requirements in the job descriptionincluding the hard and soft skills, the organizational culture, industry expertisebefore hitting Send. Even better, use a free tool likeJobscanorRsum Wordedto compare your resume to a specific job description, make changes, add the right keywords, and get past applicant tracking systems.

Provide work samples:Provide a link to samples or aportfoliothat reflect your work and are representative of how you work and communicate with technical and non-technical audiences.

Be sure to include certifications, coursework:You should includetop certifications,as well as coursework and participation inhackathonsandcompetitionsthat demonstrate expertise in must-have technologies and a passion for continuous learning. (In addition, more specialization and skills will allow you topotentiallynegotiate for a higher salary.)

Membership has its benefits. Sign up for a free Dice profile, add your resume, discover great career insights and set your tech career in motion. Register now

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Great Resignation: How to Be Successful in Attracting Top AI and ML Talent – EisnerAmper

It has always been challenging to find the top artificial intelligence (AI) and machine learning (ML) talent -- and todays environment has only heightened the difficulty. Non-tech companies have increased their demand for these workers, even as tech powerhouses like Google, Facebook and Amazon seek to hire thousands. Companies must broaden the funnel of potential candidates by making themselves more appealing to potential top talent workers. Despite the high demand for AI and ML workers, essential skills, expertise, and experience are scarce. According to a recent Gartner's 2021-2023 Emerging Technology Roadmap Survey that cited talent availability as the main adoption risk factor, it's no surprise that AI and ML professionals are in high demand at companies utilizing (or seeking to utilize) these emerging technologies, regardless of whether they're just getting started or have a lot of expertise. Its essential for firms, both tech and non-tech, to be creative in their approach.

Here are three tips for recruiting AI and ML talent:

Find where talented AI and ML engineers and data scientists hang out. For many companies, at first, this can be difficult to identify, but through resources like Meetup.com, firms can find groups where engineers and data scientists congregate. Meetup.com is a platform where groups of users focused on a certain topic get together and organize events and is a site which has been used to build professional tech community groups. Firms can find dozens of AI and ML networking communities in all large cities. Its important for firms hiring managers to be involved in networking within these groups and socialize, letting other users know why your firm is the best place to work!

Invest in creating partnerships with top tech universities for recruitment. An example of this is collaborating with data science graduate programs at local universities. From there, you have the first pick of the top talent straight out of the universities. This can provide an unlimited technology talent pipeline and connect you with the best student picks.

When interviewing the talent, its important to paint a clear picture of a culture of digital innovation and share why their work is worth it. This shows the candidates that workplaces are passionate about helping transform their clients' businesses using emerging technologies. For example, give references of AI and ML use cases that team members have contributed which provides value to the firm and clients.

As you invest in AI talent promotion and development, collaborate with your human resources team members to personalize an approach to implement AI skills at work to meet the changing expectations that the industry faces. This will lead to the promotion of internal team members and provide them the advancement of AI skills and training needed to take on roles like data scientist, data engineer, ML engineer, and business intelligence analysts.

Finally, an open innovation culture attracts top tech talent, regardless of the individuals race, gender, or background. It shows firms are passionate about the solutions they build that drive a fantastic client experience. When it comes to recruiting AI and ML talent, firms should no longer try to compete with the big tech companies like Amazon, Microsoft, Facebook, Google, and IBM. However, a more viable approach is to collaborate with leading technology companies, allowing teams to work on best-in-class AI and automation solutions from these big tech companies. With the digital revolution that COVID-19 has kickstarted, there is an opportunity for all companies to establish a strong reputation for digital excellence through the recruitment of an open, innovative, and diverse new workforce. As your reputation gets better, your opportunity to attract top AI and ML talent will be more significant.

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Over two-thirds of UAE workers believe AI and data science will impact their role in the next five years – Intelligent CIO

The YouGov study commissioned by Dataiku reveals the country to be pro-AI and ahead of its EMEA peers on awareness and action but work still needs to be done to democratize the use of data and AI.

The United Arab Emirates (UAE) leads EMEA in the leverage of data, data science and Artificial Intelligence (AI) for the purposes of decision making and business growth, according to a report commissioned by Dataiku, the platform for Everyday AI.

The survey was conducted by YouGov in early 2022 and involved 2,487 decision makers from across France, the United Kingdom, the Netherlands, Germany and the UAE. It revealed UAE respondents to be the most convinced of datas utility in the workplace, with 84% considering it essential compared with the EMEA average of 69%.

The UAE has long been established as a world leader in Artificial Intelligence, having been the first nation to appoint a minister of state for AI. And in early March 2022, Dubai International Financial Centre (DIFC), in collaboration with the UAEs Artificial Intelligence Office, announced the launch of a special license for companies that focus on AI. In support of the countrys Artificial Intelligence Strategy 2031, the license rewards firms for their efforts by granting Golden Visas for select employees.

YouGovs Dataiku-commissioned report reveals that some 71% of UAE respondents have been using more data over the past five years, a figure that is again significantly higher than the EMEA average of just over half (55%). Some 71% of UAE respondents believe they will use even more data in the next five years compared to a EMEA average of 52%.

The survey also showed a marked awareness in UAE respondents of the role AI can play in the coming years on their own jobs and within their organisation and sector. 66% of UAE respondents believe AI and data science will impact their role in the next five years, an almost equal number (65%) expect AI and data science to impact their company and 67% expect AI and data science to have an impact on their industry in the next five years.

The findings in our report clearly establish the UAE as highly aware of the power of data and AI, said Sid Bhatia, Regional Vice President & General Manager for Middle East & Turkey, Dataiku. We believe this a direct consequence of the governments forward-looking position on these technologies. We see this in its Artificial Intelligence Strategy 2031; we see it in the federal government becoming the first to appoint a minister of state for AI; and we see it in DIFCs move to issue special AI licenses. Our findings also highlight the widespread acknowledgement that enterprise AI is an organisational asset that will define the business of the future and the industries of the future.

However, Bhatia also pointed out the gap in perception between managers and non-managers in the UAE when it comes to data utility and the role of AI. While 71% of UAE managers say their use of data in daily work has increased over past five years and 73% believe their use of data will increase over next five years, only 44% of non-managers say they have used more data over the past five years and a mere third expect to use more over the next five years. Two thirds (67%) of those in management positions believe AI and data science will impact their roles but less than half (44%) of non-managers believe the same.

There is clearly a need to democratize the use of AI if it is to gain widespread acceptance as a tool of prosperity, continued Bhatia. It is only when all people within an organisation see AI as a partner in change that they will come together and collaborate. Then stakeholders can deliver the culture needed to build a digital business. Through this culture change comes Everyday AI, where organisations can truly capitalise on data science to gain the kind of insights that lead to innovation.

An Everyday AI culture is one where the leverage of data becomes routine through a combination of upskilling, governance and technology procurement. Under such conditions, execution becomes faster by including more people in the analytics process, resulting in quicker identification of opportunities, more rapid attainment of insights and slicker action.

Under Everyday AI, the use of data becomes almost pedestrian, Bhatia said. AI is so ingrained and intertwined with day-to-day operations that its just part of the business, rather than being used or developed by one central team. That is the future we see for forward-thinking business communities like the UAE.

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Python is Insufficient for Data Science! That’s Why Google has Swift – Analytics Insight

Will Googles Swift for TensorFlow will end Pythons run? What does it mean for data science?

Python was released in the 1990s as a general-purpose programming language. Pythons rise in popularity has a lot to do with the emergence of big data in the 2010s as well as developments in data science, machine learning, and artificial intelligence. Businesses urgently required a language for quick development with low barriers of entry that could help manage large-scale data and scientific computing tasks. Python was well-suited to all these challenges. But despite the growing demand for machine learning and AI at the turn of this decade, Python wont stay around for long. The emergence of newer programming languages such as Swift, Julia, and Rust actually poses a bigger threat to the current king of data science.

Swift for TensorFlow is arguably a technically superior system to what is available with Python because it uses automatic differentiation (AD) to generate novel static graphs and custom GPU code for different ML problems. There is a project for Python called Myia that aims to allow something similar, but its outside of the general Python workflow because Myia isnt really Python, but a subset of Python that requires its own compiler to create Python extensions. Flux.jl for Julia is the only thing that I know of that is competitive with Swift in the area of AD.

Swift is an open-source, easy, and flexible programming language developed by Apple for iOS and OS X apps. Swift builds on the best of C and Objective-C, without the constraints of C compatibility. Its actually a friendly programming language for freshers because of its concise yet expressive syntax and lightning speed to run the apps.

Swift has recently started gaining traction among the data science community. It is highly endorsed by Jeremy Howard (fast.ais co-founder). There are various libraries for performing tasks like numerical computation, high-performance functions for matrix math, digital signal processing, applying deep learning methods, building machine learning models, etc.

Top Swift Libraries for Data Science

According to the official blog post by the TensorFlow team, Swift for TensorFlow provides a new programming model that combines the performance of graphs with the flexibility and expressivity of an eager execution, with a strong focus on improved usability at every level of the stack. Note that this isnt just a TensorFlow API wrapper written in the Swift language. The team has added compiler and language enhancements to Swift with the aim of providing a top-notch user experience for data scientists and machine learning developers.

People use these languages for various purposes. Like how swift is perfect for developing software for the Apple ecosystem, Python can be primarily used for back-end development. While the performance of the Swift and Python vary, Swift is faster than Python.

When a developer is choosing the programming language to start with, they should also consider the job market and salaries. Comparing all this, you can choose the best programming language.

The fact of choosing Python or Swift for coding mostly depends on the purpose. If you are developing applications that will have to work on Apple OS, you can choose Swift. In case you want to develop your artificial intelligence or build the backend or create a prototype you can choose Python.

In conclusion, nobody is suggesting that Google is out there seeking to kill off Python, but its obvious that they have found the limits of the language for data science. Given their tremendous investment in machine learning, it makes sense that they would reach these limits before anyone else. The question really is how long until you too reach those limits?

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Tredence: Data must be milked if its to have any worth – The Financial Express

The phrase Data is the new oil is often used today to depict the rising value of data for businesses across sectors. However, the saying also depicts a key gap just as crude oil needs to be converted to forms like petroleum for use, data needs to go beyond insights to actually drive action on the ground. Whats my data worth? this is a question that leaves company executives in a cold sweat, says Shub Bhowmick, co-founder and CEO of Tredence. Forward-looking CFOs in large enterprises often ask their business leaders about the ROI of analytics investments. In reality, most organisations are betting their money on siloed and misleading data.

Bhowmick, who has an MBA from Northwestern Universitys Kellogg School of Management and a B Tech in Chemical Engineering from IIT-BHU, explains that every day, 2.5 quintillion bytes of data are created. However, most organisations struggle to unlock the value trapped in data due to a lack of strategic focus, domain understanding, and far-reaching innovation.

The founders of Tredence thus saw a gap between insights delivery and value realisation that needed to be filled. Tredence, a data science and AI engineering company, is focussed on solving this last-mile gap in data analytics. The decision to start a new business germinated while Bhowmick was based in the Silicon Valley. I discovered that Fortune 500 companies were looking for partners that could deliver data analytics services and create value in terms of quality and at scale.

After he met co-founders Sumit Mehra and Shashank Dubey, the idea took concrete shape and Tredence came into existence in a three-bedroom flat in Bangalore in 2013. Today, we have a presence in six countries and 10 offices worldwide with over 1,600 employees, who work with some of the worlds leading firms in retail, CPG, hi-tech, telecom, healthcare, travel, and industrial sectors, solving some of the most complex and high-impact problems they face, he says.

Tredence builds data science solutions with a vertical-first mindset and an outcome-driven attitude that enables large-scale transformation for clients and equips them for accelerated growth. Tredence Studio is its co-innovation platform with 50+ ready-to-deploy accelerators which helps businesses plug gaps in their business value chain through intelligent capabilities in the absence of off-the-shelf solutions. For example, the healthcare industry is aspiring to transition from fee-for-service (FFS) to value-based care. However, we recognised the challenges that healthcare providers and payers were facing in improving outcomes and optimising the costs of care. Tredences HealthEM.AI built by medical science and data science professionals sparks value-driven strategies for the new healthcare ecosystem and delivers enhanced patient experience by turning data into actionable insights, says Bhowmick. In the industrial manufacturing sector, Rebate.AI, the firms B2B rebate management platform, helps trading partners forge meaningful relationships and expedite rebate return on investment.

In December 2020, Tredence secured $30 million in capital from Chicago Pacific Founders (CPF), a premier private equity group. With a CAGR of 50% since its inception, it is one of the fastest-growing data science companies in the world, claims Bhowmick. It grew by 65% in 2021, in the process providing young AI professionals the boost they need for accelerated learning in technology, analytics, and business consulting, he adds.

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Ravio is bringing real-time data to the talent fight – TechCrunch

UK-based Ravio reckons real-time data is the best way to arm businesses to win the global talent war.

Its new-to-market compensation benchmarking tool lets users see how the compensation (wages and benefits) they offer their own staff compares to the market by pooling data across its employer customers (data is anonymized at the platform level) idea being to help them offer more competitive packages which help convince talent to sign on the dotted line.

Customers can explore market data and compare themselves to companies with similar characteristics such as location, industry, funding raised and headcount growth, it writes of its SaaS platform in a press release accompanying the official launch today.

The software also lets companies identify compensation inequalities at individual, job type and company levels and compare to peers in the market, as it tells it.

Whether it be gender, background or other criteria, this offers the most detailed diversity insights available to companies who want to make data-driven changes, is another top-line claim.

The startup, founded out of London, has been operating in stealth mode since starting building work on the platform in January, though it quietly opened up access a couple of weeks ago. (Research work and the idea itself dates back to last year.)

It is now flipping on the light today for its official launch and also announcing a total of $10 million in seed funding led by Northzone with participation from Cherry Ventures and Spark Capital.

Ravios first target is fast growth scale-ups which are of course at the coal-face of the fight for talent. These high profile, high pressure startups are also often on the front line for diversity issues where Ravio believes its SaaS tool will also provide valuable benchmarking.

Hiring and retention is the biggest challenge right now for scaleups due to war for talent, fast moving markets, (over)funding, the shift to remote work and the great resignation, argues co-CEO and co-founder, Raymond Siems.

Theres a lot of clamour on closing pay and diversity gaps at startups but not many tangible numbers or practical solutions its mostly high level reports and talk.

The follow-on goal for Ravio is to deepen its database by getting more scale-ups signed up and, ultimately, it hopes to be able to expand the approach to other sectors. After all, notes Siems, there are talent shortages across all sorts of sectors so a tool that supports businesses to make more attractive compensation offers could have broad utility, assuming the benchmarking insights live up to billing.

Were starting focused but the need for a solution is universal across industries and company sizes, its not confined to the tech industry. We validated this during our research phase, with strong feedback from SMEs all the way up to large enterprises in other industries, he tells TechCrunch.

High growth startups are the best place to start because theyre the least served by traditional benchmarking providers, and are feeling the talent crunch the most. Employee equity in private companies is one of the least transparent areas within compensation, so were tackling this early on.

By having a focused approach we can build depth in our dataset, before expanding into other sectors.

Ravio works by a sharing to participate model which means that organizations that want access to its jobs benchmarking data must agree to share info on their own teams, including salary data.

The basic service is free but the team is building additional modules to layer on top that will be paid so its taking a freemium approach to grease its data pipe.

We gather data from source of truth systems including the HRIS, ATS and cap table management software of our customers, explains Siems. We primarily connect via API, so theres no manual work for our customers in submitting and drawing together data, and this enables real-time updates rather than one-off static submissions which are inefficient and hard to scale.

We currently support 28 HR systems which cover all the major players in Europe including Workday, Personio, BambooHR, HiBob and Namely, he adds.

Per Siems, Ravios automated, scalable data-pipeline approach is made possible because of widespread accessibility of employer data held in HR and other business systems via API it just needs to convince employers to give it API access.

He does not sound concerned that Ravio wont get a critical enough mass of sign-ups to be able to generate genuinely valuable compensation insights. Were set to expand quickly so the market coverage of our dataset will enable us to provide the most useful and granular insights available in Europe, and the only-real time information, he responds to a question on how comprehensive a view of the market Ravio has at this stage, before reiterating that the benchmarking and compensation analytics product is free.

Its a self-service onboarding that can be done in 15 minutes so its as simple as possible for companies to join, he adds.

Siems also says Ravio is doing some of its own processing of the data it acquires from user companies so that it can extrapolate beyond our market penetration.

This special sauce incorporates macro data to predict wider trends, per Siems, who points to what he says is the most sophisticated compensation work currently being done by internal teams at big tech players like Meta as the teams inspiration. He says Ravio has also hired people who have experience working on compensation for Big Tech firms including Amazon and Meta, as well as other multinationals like Coca-Cola.

They buy data from all of the major consultancies and then do their own data science work to build up a more detailed and comprehensive picture. We want to enable this depth and sophistication for companies of all sizes, he says, adding:Were bringing a data science first approach into an area where there has been very little innovation, with a strong data science team from Cambridge and Oxford.

Ravio isnt disclosing the total number of scale-ups which signed up pre-launch but Siems, claims its already got a healthy list of customers who heard about what we are building and wanted to join our testing phase.

Some of the names were working with include unicorns Deliveroo, Truelayer, Flink, Zego and other high-growth European startups including Healx, Zoomo and Plum Guide, he says.

The paid version of the SaaS will be charged based on the number of employees the user has, according to Siems.

Were charging for advanced features and two additional modules that were releasing in the coming months: Manage and Communicate, he notes, describing two of the premium products it has in the works. Our Communicate module includes interactive offer letters for candidates, and a compensation portal for employees, to make it easier for them to understand every part of their package and their employer. This includes education on commonly misunderstood topics like equity and tax.

Our Manage module provides a suite of tools to allow companies to manage compensation processes effortlessly. This includes organisation level budgeting and banding features, compensation review cycle management workflows, and growth scenario planning.

On diversity, how full spectrum Ravios proposition can be is less clear given Siems says the data available to it varies by company.

Were seeing the most commonly available data relates to gender, age, country of citizenship, marital status and ethnicity but increasingly HR systems are supporting wider data fields such as disability status, so well be expanding our analytics as the data allows, he says on that.

While employers having better data on competitive rates of pay and broader compensation packages might be good for businesses to win talent, there is a question of whether this necessarily benefits employees who arent being directly empowered to, for example, call out wage gaps inside their own organization, say if certain groups are being paid below market rates, since the tool looks intended for use by the HR department, rather than for general access.

Asked about the risk of one-way real-time information disproportionately empowering employers, Ravio says: We will be releasing market reports that are freely available covering both compensation and diversity that will empower both candidates and other employers.

We believe that many of the current inequities in the market are not a result of bad intentions but rather the result of a lack of data, he adds. By becoming an objective source of information for employers in a notoriously opaque and difficult to understand space, Ravios product will ultimately benefit employees by empowering companies to make unbiased decisions.

Flush with seed funding and post-official-launch, the teams focus for the rest of this year is on growth in Europe where Siems says they spy huge opportunity.

However the talent war is global so we will be quickly expanding our reach to new shores next year, he adds, noting the startup has grabbed backing from a number of international investors. He also flags the experience of his co-founders, Roy Blanga and Merten Wulfert, in helping to rapidly scale Deliveroo, Groupon and HotelTonight internationally claiming: Were well positioned to execute on our vision.

On the competitive front, Ravios view is theres very little on the fairness and team analytics side with most of the action focused on compensation benchmarking. So it will presumably be weighting its offering there.

In some ways we are building a new age version of salary surveys and compensation benchmarking services offered by Radford, Towers Watson and Mercer (the big 3 players) which have been around for decades, Siems suggests, adding:There is no one offering real-time data in Europe to our knowledge. Some new players are serving tech companies but only with static, manually collected data. In the US there are some new players and older static offerings like Option Impact.

Its also possible that HRIS players (e.g. Personio) will look to build their own version but with the market for HR tech being so fragmented it will be tricky to get worthwhile breadth of data long term. The market is better structured for there to be a HRIS/ATS/Cap table agnostic player like us.

Glassdoor does already pool user-contributed salary data so can offer a snapshot of pay rates but Siems believes Ravio is not directly competing since Glassdoors data is unverified, patchy, collected via one-off submissions and lacks enough breadth or depth to be useful to companies making compensation decisions.

Companies need comprehensive data, he argues. It also doesnt have access to information to enable team or fairness analytics.

Commenting on Ravios seed in a statement,Michiel Kotting, partner at Northzone, said: We see a huge market shift happening which is going to leave behind companies who dont modernise their approach around compensation. Winning companies will be transparent with compensation in the context of rising prices (inflation, cost of living, logistic costs) and a tighter talent market, especially in tech. Roy, Merten and Raymond are going after a problem that all companies big or small have, and are poised to build a leading company in the category. They have the experience and leadership to tackle all things compensation from salaries to equity and benefits, all big pain points for fast-growing companies.

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