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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 Businesses without a proper data science operation may soon look as out-of-date as companies that rely on fax machines and floppy disks.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.

Read the Full Post Here

We’ve all seen the hype around Enterprise Big Data and AI build up over the last few years, culminating in a record year of investments, conferences and implementations in 2017. But how real is AI when it comes to building value for your business today and over the next five years?

Although we are certainly many years away from a human-like AI as we see in the movies; today, narrow or domain-specific AI technologies are already making an impact on bottom lines. Companies that have been smart about adoption and able to quietly implement AI-aided solutions into various functions such as Demand Planning and Inventory Management, Back Office Processes, Sales and Marketing are reaping the benefits.

Because AI can help companies find competitive advantages, demand is increasing at an incredible pace. New companies offering AI enabled software, and other technologies seem to pop up almost daily. Considering the amount of money and brainpower poured into AI research, it won’t be long until commercializing and monetizing data using AI as well as transforming internal processes becomes a necessity to remain competitive.

According to the recently published Teradata report State Of Artifical Intelligence For Enterprises, the majority  “see AI as being able to revolutionize their businesses, automating repetitive processes & tasks and delivering new strategic insights currently not available.”

But with most enterprise software initiatives taking on average 21 months to implement and with Big Data and AI being at the complex end of the spectrum, it is no surprise that 91% see barriers ahead with lack of IT infrastructure (40%) and lack of talent (34%) as the most significant.

So how do you quickly adopt AI successfully across different business functions, driving real and immediate ROI?

AI as a Service

AI Software As a Service (SaaS) adoption is a clear trend that is taking hold in enterprise technology stacks. Adopting SaaS solutions can help companies smooth out their revenues, leading to more resilient and flexible organizations, ultimately allowing a company to deliver better service and products to their clients. With a shortage of talent in this arena and the large data sets required to effectively train artificial intelligence algorithms and implement them into production software, the SaaS model has clear advantages versus trying to develop all capabilities in-house.

Definitive Advantages

The reasons for moving to SaaS offerings can be different for each organization. One of the primary drivers is the potential to create a technology advantage over established competitors and potential disruptors.  Others find they’re increasingly dissatisfied with the way their legacy functions and processes run, and want a better and faster way to see improvements.

Services are defined based on business results and can be expected to produce value quickly, be flexible, implemented quickly, and paid for based on value, business outcomes, or on a seat/consumption basis. This approach leaves more room for pivoting if the ROI is not there as promised, in contrast to traditional capital investment projects where teams often fall prey to the sunk cost fallacy or have a hard time measuring the ROI of their investment.

Enterprises that transition to this model will have a definitive advantage over those that don’t. Companies that don’t shift to aaS models will see their ability to compete diminished, and the same can be said about leveraging AI enabled technologies such as Robotic Process Automation and Automated Insights Generation to name a couple of tangible applications of AI in the enterprise today.

A SaaS tech stack also offers a company greater agility. Traditional industries are consolidating amid increasing mergers and acquisitions, and that means becoming more agile and lean to compete and continue to grow. Service-based models allow companies to trim infrastructure, creating flexibility to scale up or down depending on business needs.

A SaaS model also enables better analytics to derive business insight and help make performance improvements. With clear and contained costs and sometimes built-in analytics capabilities, it is easier than ever to evaluate business results and ROI of investments in services vs. traditional Capex expenditures.

Getting There

Determining how to start adopting AI technologies as well as transitioning to a SaaS and multi-cloud based stack is not necessarily easy. Where to start? With a single problem, department or business need, or do you embark on an enterprise-wide effort?

It can be as simple as starting small with low-hanging fruit and then expanding from there. Is there a department that is last on the priority list for IT but could make some significant gains if given the right tools today? Is there an apparent cost, margin or process that can be identified for measurable improvement? Companies that have seen immediate success often start small and then build on that success. Technology moves too fast these days to allow for extensive planning and execution timelines.

No matter how they get there, in the long run, businesses that transition to service-based models have incomes that are more consistent over time, allowing them to make better and more agile decisions that lead to robustness, flexibility and therefore long-term sustainability.

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.

Analogue: Scalability in Data Usage

At the intersection of big data and machine learning are patterns and analyses that reveal trends and causes. To use healthcare as an example, sensors built into wearable medical devices open windows to improved, individualized healthcare based on a rapidly expanding set of clinical, lab, physiological, and personal data. (A patient diagnosed with hypertension might wear a device that sends information to an application that detects ongoing changes in blood pressure, respiration, or other conditions in real time and alerts a physician when anomalies occur.)

Predictive data technology moves past the goal of gaining insights and into the realm of insights on insights: namely, choosing the trends that require action. If the information received from the wearable monitor is utilized as cross-channel data, the challenge becomes making sense of the insight gained from the data and selecting the appropriate action. With this, the perspective may move from a simple focus on the instantaneous symptoms and treatment of hypertension to a holistic view of the patient’s respiratory, renal, and other systems’ response to standardized treatment.

The Human Factor

The relevance of obtaining cross-channel data from a hypertension patient is most apparent in the universal desire for individualized care. Scalable machine learning searches for efficient algorithms that can work with any amount of data and detect hidden insights. These insights yield logical, adaptive reasoning in performing specific actions, without consuming greater amounts of computing resources. Limits do exist, but predictive data technology adds another dimension to the interpretation of vast data sets. One that, in a business context, means greater efficiency and more thorough self-evaluation on a global scale.0

In the marketplace, insights gained from cross-channel data emphasize the individual’s ability to change. While individuals may defy—with varying levels of deliberateness—predictability, machine learning and predictive data technology take an unrestrained, multi-dimensional view of preferences, real-time behavioral patterns, and possible intent. Thus the view of the “customer journey” is expanded: and a mass of stops at a big box store from which a correlation would have normally been determined in retrospect is now a targeted real-time marketing effort—with the intuition to make progressively better use of progressively expanding data.0

Moving to a New Meaning

Terms like “segmentation analysis” and “adaptive marketing” are themselves harbingers of a system that will soon replace the marketing philosophies of old. However, these new practices may themselves prove to be stepping stones to an even broader view of personalized marketing. Real freedom from scale is measured over time: through predictive data technology that offers personalized strategies for small businesses, large businesses, and corporations as they grow. This new outlook recognizes the consumer’s awareness of the marketplace and the complexity of their decisions, providing insights into profit margins based not only on the instantaneous relationship between product and cost, but also by an adaptive view of long-term customer behavior and loyalty.

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.

There are many trends coming to the foreground of AI, machine learning, and business intelligence in 2017. This article will be talking briefly about these trends and a link to the in-depth report by Tableau can be found at the bottom of the page.

BI (Business Intelligence) the New Norm

In 2017 we will see a trend of more and more companies using modern business intelligence, allowing analytics to be performed by all employees, not just data scientists and engineers.

Collaboration between Machines and Humans Strengthens

The collaboration and sharing of data is going to move from one-direction, spreadsheets and emails, to an interactive flow of data between multiple parties and their live data stream.

Data Will Become Equal

All data will be equally accessible and understandable. We will be able to access all our data without the worry of it being stored in the same format.

Anyone will be able to Data Prep.

Just as self-service analytics is becoming more accessible to non-technical employees, so will the ability to understand and prep data without the need of a technical background.

Imbedded BI is Allowing Analytics to Grow Everywhere

Business applications like Salesforce are placing analytic tools in the hands of people never before exposed to data. These tools are extending the reach of analytics in our day-to-day lives and we most likely are unaware that we are using them.

Work with Data in a Natural Way

In the next year we will see people being able to access and communicate with their data in a more natural way. We will see this more with the integration of natural language interfaces within AI networks.

Cloud Based Analytics

With data being stored in the cloud we will soon see analytics being conducted there as well. Cloud analytics will be faster and able to scale at a much quicker pace.

Data Literacy will Become a Necessity 

With Data analytics and predictive analysis moving to the mainstream we will see a need for all level of employees needing to be able to read and understand their company’s data.

 

Read the full report by Tableau Here:

https://www.tableau.com/learn/whitepapers/top-10-business-intelligence-trends-2017