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 Businesses without a proper data science operation may soon look as out-of-date as companies that rely on fax machines and floppy disks.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

March, 2018, Insights Success Magazine

The average knowledge surrounding Artificial Intelligence revolves around a rumored headline of it surpassing human brain with the help of a computer generated program, causing a cloud of catastrophe over the idea. But the reality is much more complex than the above stated rumor. Organizations from varied sectors realize the potential of AI and Machine Learning and are racing against each other to build the most efficient AI enabled program, capable of reducing human efforts by solving the maximum tasks possible. Built on the concept of people and machines working together, Algomus is one company that stands out to create an environment where humans and machines can work and learn from one another to make specific tasks easier.

Source : The 10 Most Promising BI and Analytics Solution Providers 2018

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.