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 Businesses without a proper data science operation may soon look as out-of-date as companies that rely on fax machines and floppy disks.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

At Algo.ai, we’re proud to stand at the forefront of AI research and development. That’s why we’re excited to share some of our research on prescriptive analytics — combining ideas from machine learning and operations research to develop a framework for using data to prescribe optimal decisions.

Descriptive analytics is one of the first steps in data processing: summarizing historical data to produce useful information and prepare data for further analysis. Predictive analytics utilizes statistical techniques such as data mining, predictive modeling and machine learning to analyze historical facts and make future predictions. Prescriptive analytics takes elements of both descriptive analytics and predictive analytics to turn data into optimized predictions.

Operations research and management science typical starts with models and aims to make optimal decisions, leaving the data itself as an afterthought. Conversely, machine learning and data science often begin with data in order to make decisions.

The opportunity that these methods present is to take the significant amount of available data — often big data in electronic form — and develop a theory that unifies operations research and management science that takes data from predictions to prescriptions. The more data we have, the more optimal decisions can be made.

In one real-world example, take the case of a global Fortune 100 media company that sells nearly 2 million different CD, DVD and Blu-ray titles at over 50,000 retailers worldwide. Faced with limited retail shelf space, an endless array of titles and uncertain demand for new releases, how can they best choose which titles to order and in what quantities to maximize the amount of media they sell?

Using a wealth of data and our prescriptive analytics methodology, this company can use prescriptive analytics to fill in gaps throughout the decision-making process to find the best titles to promote. Data was obtained from all available sources, including four years’ worth of sales data spread across their 50,000 retail partners.

Next, data was sourced from online sources such as IMDb and Rotten Tomatoes to determine factors that indicated a movie’s popularity — its actors, Academy Award nominations and box office statistics. Finally, more data could be pared down by location to discern a particular title’s popularity in any given region.

With prescriptive analytics, all data can be considered useful. Even though some data may be more helpful than other data, it can help inform the best solutions and open up previously unseen ways of thinking.

For complete insights into how prescriptive analytics works, download our scientific research paper.

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

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

Do not Fear AI

Is AI the destructive force that will destroy all jobs and the world as we know it? The media and Hollywood have depicted AI as such, however this is not the case at all. At this point in time, machine learning and AI has become a daily tool in business intelligence. These tools are giving time back to their human Analyst counterparts. Analysts are using machine learning and AI software to better understand their company’s data in a more timely fashion.

Liberal Arts Impact on AI

In the upcoming months Liberal Arts will be playing a bigger role in the building of AI and machine learning software. Data scientists are realizing they not only need the data analyzed to be accurate but also tell a story that anyone can understand, including those without a technical background.

NLP (Natural Language Processing) Promise

NLP refers to the way we interact with the AI through the UI (user interface). Companies are beginning to want all level of employees to have access to the data provided by their AI software. The problem many of these companies face is that most of their employees do not have a technical background and no idea how to query a piece of data. This is where NLP comes into play; AI software can process queries in natural language instead of using specific codes. e.g. I want to know the Sales for Item “001”  by day at Store “2045”

Multi-Cloud Capabilities

The move to multi-cloud storage is becoming an ever-increasing desire within big companies. Companies don’t want to be limited to one storage method that may not provide the best performance for their data needs. Though multi-cloud architecture has many benefits, it also has its costs, one of which being the actual overhead cost of running this type of multi-cloud environment.

Rise of the CDO (Chief Data Officer)

With understanding data and analytics becoming a core competency more and more companies are creating a position of CDO. This position allows them to join the C-suite with the CEO, CTO and CIO. This new position gives the CDO the ability to attend the C-level meetings and actually affect change within the company. Due to the creation of the CDO position, companies are showing just how important it is to understand their data and manage it successfully.

Crowdsourcing Governance

Crowdsourcing governance is a fancy term for allowing customers to shape who has access to specific data within a company using self-service analytics. It gets the right information into the right hands while keeping that same information out of the wrong hands.

Data Insurance

Data is more valuable than ever. We have seen countless data breaches over the last few years and will most likely see many more. With customer data becoming so valuable we are going to see a rise in data insurance. This insurance will protect companies from being responsible for a breach of their customer data.

Data Engineering Roles

As data analysis software continues to grow in use and value we will see a rise in data engineering roles over the next several years. Data engineers will begin to transform from more architecture-centric roles to a more user-centric approach within their organizations.

Location of Things

“Location of things” is in connection to IoT (internet of things). We are seeing companies trying to capture location-based data from IoT devices. Gartner, predicts there will be 2.4 billion IoT devices online by 2020. The problem is that companies are trying to collect and compile all this location data within their internal data structures, while most of these structures are not capable of accepting that quantity of data. This is going to lead to great innovations for IoT data storage.

Academics Investments

With data analytics growing in all industries the demand for future data scientists will continue to grow. Due to this high demand for data engineers and data scientists we will begin to see more and more universities offering some sort of academic training in these categories over the next several years.

 

Read the full report by Tableau Here:

https://www.tableau.com/reports/business-intelligence-trends#ai