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Across the world, the demand for data scientists is skyrocketing — and so far shows no sign of slowing down. Experts predict that in the United States alone, there will be more than a quarter of a million open positions for data scientists by 2024 as data scientists continue to become an essential component of the modern workforce. Businesses without a proper data science operation may soon look as out-of-date as companies that rely on fax machines and floppy disks.

That’s because data scientists are helping organizations solve problems and make decisions through scientific analysis backed by clearly defined insights. The right data science operation can steer companies away from taking unnecessary risks or making huge mistakes thanks to a team of experts on hand ready to connect the dots and comb through every piece of usable data to come to draw a proper conclusion. 

But building a quality data science operation isn’t as simple as hiring a data scientist and calling it a day. Increasingly, organizations are having trouble making the most of their data science operations because they haven’t made the proper structural or organizational changes to their company.

new report from the Harvard Business Review points to data teams armed with incredible insights that aren’t able to properly communicate their findings to non-technical audiences, such as executives. Valuable research and analysis that aren’t properly conveyed ultimately leave people confused and unable to comprehend the scope or conclusion of the work. The result is a fundamentally misunderstood data operation that leaves decision makers questioning the value of their investment. 

The solution to creating a functional and productive data operation is to think beyond the data scientist alone and build a team of experts with complementary skills. Bringing data to life means working with designers, subject matter experts and storytellers who can properly convey the message that lives within your information. Working collaboratively, a properly assembled data science operation can help fix blind spots, make data seem as compelling as possible and convince stakeholders of the necessity of your work.

At Algo.ai, we’re proud to be doing just that. Our team — comprised of some of the world’s leading artificial intelligence experts, software engineers and industry domain experts — works together to bring data science to life for our clients. By embedding data into the DNA of our work, our team members can share their collective talents with our data scientists to help make the best data-driven decisions possible.

January 16, 2018, RE•WORK

Algomus is a rapidly growing Detroit area startup that has built Algo the world’s first Analyst Workbot and Enterprise ML powered Big Data Analytics platform for media & entertainment companies, manufacturers, distributors, and retailers. Algomus, who is partnering with RE•WORK for the AI Assistant Summit in San Francisco this January 25 – 26 have just launched Algo 2.0, making their AI enabled workbot even smarter.

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Building a data science team may seem like a daunting task, especially in this market where talent with practical experience is scarce but interest and buzz in the field is extremely high. Here are a few tips for building and running a successful Data Science team.

Find the Right People

What roles must you fill for a complete data science team? You will need to have a variety of people with different types of skills:

  • Data Scientists who can work with large datasets and understand the theory behind the science. Most importantly they need to be capable of developing predictive models that fit your business context.
  • Data engineers and software developers that understand architecture, infrastructure, and distributed programming.
  • Other roles include a data solutions architect, data platform administrator, full-stack developer, and designer.

Build the Right Processes

The key thing to consider with data science workflows is agility. The team needs the ability to access and work with data in real time. The team then needs to be able to understand business problems and opportunities in the company and implement data solutions that solve those problems or facilitate growth. Make sure they are not handcuffed to slow and tedious processes, as this will limit effectiveness and make it harder to retain top talent.

Finally, the team will need to have a good working relationship with heads of other departments, and clear executive support, so they can work together in agile multi-disciplinary teams to deploy solutions that really benefit the business and will ultimately be adopted by business users.

Choose the Right Platforms

When building a data science competency, it is essential to consider the platform your company is using. A range of options is available from open source to paid services from major cloud providers and innovative startups.

We recommend you maintain some flexibility in your platforms because business and technology moves fast, and you don’t want to tether your team to a tech stack that could become a limitation to their growth and flexibility. Hybrid architectures that utilize the right technologies for the right applications are ideal. Talented architects should be familiar with many different technologies and methods and understand how to select the right components for current and future use cases.

Take Your Time

Most importantly you don’t want to rush and choose the wrong people and platforms or not have quality processes in place. Make sure to take your time to create a team that will work well together, has complementary skills, understands your business, and can deliver successful outcomes that get adopted by the business.

Ensure the Team’s Success

Once you have assembled the right team here are 5 things to keep in mind to maximize the impact they can have as they start building data-driven solutions to give you a competitive advantage:

Discoverability

Data science teams that are not practicing discoverability are writing scripts to solve different functions and not publishing them in a common place. In order for anyone to access this information it usually requires contacting one of the data scientists directly and having them send it over in a presentation or excel sheet. This is both a waste of time for the person asking, and the data scientist that has to devote time to re-delivering rather than innovating. A team that is successfully practicing discoverability publishes their work in a central location where everyone in the organization has access to it.

Automation

The difference between a data science team that does not focus on automation and one that does is quite simple; the team that does not focus on automation is continuously producing results by hand instead of letting their models do the work for them. The team that focuses on automation spends their time maintaining the pipeline instead of manually re-running their workflow. While automation can take more time up-front, it pays off in multiple ways when done successfully. Automated pipelines make it much easier to build the insights and outcomes from your team’s efforts into business processes, continuously increasing the ROI on your data science endeavors.

Collaboration

A data science team that focuses on collaboration and consistency will benefit significantly compared to those that do not. Collaboration allows for the strengths of individuals to help the group as a whole. Collaboration is much easier to achieve when there is consistency between how code is written from individual to individual. Those teams that do not have a shared set of standards will have trouble collaborating and end up with individual quality standards, versioning habits, and coding style. Collaborating with business stakeholders and users is also an important component of successful data science deployments. Great models are useless if no one can use them, users don’t trust them, or they were developed without the correct business context.

Empowerment

Data science teams that agree to use the same stack of tools are better at discoverability and collaboration as well. The trick is to get the right tech stack for the needs of everyone in the team. A team that does not have a cohesive tech stack will suffer from an over-abundance of data storage and analysis tools and a lack of collaborative cohesion. Empowering your teams with tools that make their jobs easier and facilitate the collaboration and automation will set them up for success and aid in job satisfaction.

Deployment

There is a big difference between workflow being “in production” and “produced.” Work that is “in production” means failure is ok and work that is “produced” or finished means failure is not ok. A good data science team will make sure to put tools into production that can be trusted and used to benefit the stakeholders. They will not create things just because they can, instead focusing on the problems that actually need to be solved and making the results digestible and usable by the business.

Data Science as a Service

There are also many options for engaging external expert teams that can accelerate adoption of Data Science while also preparing your organization for growing in-house capabilities.

The same principles apply to service providers and consulting teams. Make sure they are equipped to build continuous value for your organization, not just deliver one-time results.

Sources:

https://mapr.com/blog/how-build-data-science-team/

http://lineardigressions.com/episodes/2017/9/24/disciplined-data-science

BERKELEY, CA – 21 Jul, 2017 – Algomus has announced that former Sony Pictures Home Entertainment VP, Jeff Fueston, has joined forces with their executive team to lead Product and Customer Success. Jeff Fueston brings upwards of 20 years experience leading Global Customer Operations, Inventory Management, and Operations and Supply Chain Management for global industry leaders including Sony Pictures Home Entertainment, MGM Studios, and Universal Studios.

Jeff Fueston joins Algomus as Vice President of Product and Customer Success to continue their trajectory as the rising AI-powered business intelligence solution in the retail and manufacturing verticals for global industry giants such as Walmart and Sony Pictures Home Entertainment. Jeff brings a fresh perspective to the Algomus executive team: “Having been a client who implemented Algomus into our strategy while serving as VP at Sony Pictures, I know the power and value they bring to businesses that integrate their technology into their workflow. With over 20 years experience in entertainment distribution at Technicolor, Universal, MGM, and most recently at Sony, I’ve seen the landscape change dramatically over the years. Having witnessed such rapid change only reinforces my experience that Algomus helps companies drive sales and cost savings, even in declining markets. Joining the ranks of such a talented group of people counts as a career high and I look forward to helping companies benefit from the power of Algomus.”

Amjad Hussain, MIT MBA graduate and Algomus CEO, says, “Jeff Fueston’s experience is just the propellant Algomus needs to continue to solve big problems with Big Data. We are particularly thrilled to add such deep vertical expertise in the Home Entertainment space. Algomus is powered by a passionate team of designers, machine learning scientists, and engineers with tremendous industry knowledge of retail category management and discrete consumer choice behavior. With a deep passion for applied machine learning, business process automation, and enterprise mobility, it’s our mission to make sure those elements are woven into the fabric of everything we build to continue solving meaningful problems for clients worldwide.”

Algomus is an Enterprise AI company offering hosted business intelligence solutions that deliver domain-specific insights from data by leveraging the power of AI and predictive analytics.

Enterprise companies often struggle to ascertain how to optimize all the data they collect. Big data — very large data sets — makes the problem even thornier. However, new data analysis tools such as predictive analytics and analytics as a service enable businesses to quickly harness all the data they collect and turn it into meaningful insights. Self-service analytics is the future of data analysis, letting employees without technical skills manipulate data without writing complex queries.

The introduction of analytics as a service means companies can avoid paying for additional hardware, storage, power and cooling. Utilizing a cloud-based analytics platform also means there is no need to hire expensive consultants and data scientists and allows employees without an IT background to manipulate data.

Big data for better decision-making

Big data analysis, especially predictive analytics, provides businesses with a deeper understanding of customers’ motivations and internal and external factors impacting their business. This knowledge helps companies to foresee what customers will buy, an advantage that can result in greater revenues and reduced costs in comparison with other marketing efforts.

In combination with predictive analytics, prescriptive synthesis enables companies to extract the optimum operational decisions to maximize profits under uncertain conditions.

Predictive analytics delivers significant impact across the customer lifecycle and better results than retrospective analytics (looking at past customer behavior) according to a survey of B2B marketers by Forrester Research.  While 14 percent of surveyed marketers who used retrospective marketing techniques reported revenue growth higher than the industry average, 41 percent of marketers who used predictive techniques experienced revenue growth that surpassed the industry average. Forrester concluded that the use of predictive marketing analytics correlates with better business performance.

Increase revenue and reduce cost

While making better decisions provides a competitive advantage, organizations still have to evaluate whether their investment is saving them money and/or increasing revenues. The average enterprise company spends about $14 million annually on big data projects according to research firm IDG. Calculating the ROI of big data analytics can be difficult. Forbes Insights and consulting firm McKinsey conducted a survey of enterprise businesses for an analytics provider and found that 27 percent of respondents had revenue boosts of three percent or more, while 38 percent had revenue gains between one and three percent attributable to big data analytics.

On the cost side, 21 percent said that they saw a reduction of three percent or more, while 39 percent had cost reductions of one to three percent, In a smaller survey, McKinsey found that big data projects cost 0.6 percent of corporate revenues and returned 1.4 times that level of investment, increasing to 2.0 times over five years.

Maximize ROI

Big data analytics involves a lot of moving parts and variable costs, but with the availability of pay-as-you-go Analytics as a Service it is now much simpler to build and deploy customized analytics platforms with modern interfaces like self-service Visualization and Natural Language features such as chatbots and automated BI reports.

Once the complexity is reduced to a flat monthly fee or scalable usage-based fee, it becomes much easier to identify ROI quickly to understand if an investment is producing the intended ROI. No contracts mean businesses can stay flexible and pivot as needed to support their growth or take advantage of new innovations.