How to bridge the production gap between data and IT

Data scientist has been one of the superstar IT roles of recent years, with the promise of spinning data into gold with the application of AI and machine learning.

Using cutting-edge technology to extract deep insights from reams of business data, data scientists aim to help guide their organisations into a more innovative, efficient and profitable future. But so far, return on investment hasn’t always been what companies might hope.

“One of the biggest mysteries in data science today actually has very little to do with data science: What is that last mile to AI ROI?” says Sivan Metzger, managing director MLOps and governance at DataRobot. “You build your machine learning, you find the data, you get it cleaned up, you build the models, you try 90 different iterations, you make a good and clean one and it’s ready to go. What happens then? Why are we not seeing value at scale from AI?”

Metzger credits these issues to a disconnect between the data team, IT operations and stakeholders on the business side (i.e. the potential consumers of data science insights). Data science and IT operations teams have very different considerations and goals – and machine learning is very different from running software. This disconnect is known as the ‘production gap’, and can prevent AI solutions from being properly executed.

Machine Learning Operations (MLOps) is a combination of processes, best practices and underpinning technologies which seeks to bridge this gap by increasing collaboration and communication between data scientists and operations staff – and ultimately ensuring that AI is properly deployed and can begin to deliver the ROI promised.

To learn more about how MLOps can improve your returns on AI, watch IT Pro and DataRobot’s webinar ‘The Last Mile to AI ROI’, in which Metzger and data scientist Rajiv Shah discuss topics including:

• How to eliminate AI-related risks by adopting MLOps best practices
• The inherent challenges of production model deployment and how to overcome them
• Model-monitoring best practices
• Production lifecycle management and why it matters

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Dublin, June 15, 2020 (GLOBE NEWSWIRE) — The Global Data Science Platform Market study with 100+ market data Tables, Pie Chat, Graphs & Figures is now released by Data Bridge Market Research. This Data Science Platform market report serves to be an ideal solution for better understanding of the market and high business growth. It has become the requisite of this rapidly changing market place to take up such marker report that makes aware about the market conditions around. This Data Science Platform market report comprises of an array of factors that have an influence on the market and industry which are industry insight and critical success factors (CSFs), market segmentation and value chain analysis, industry dynamics, drivers, restraints, key opportunities, technology and application outlook, country-level and regional analysis, competitive landscape, company market share analysis and key company profiles

Due to the pandemic, we have included a special section on the Impact of COVID 19 on the Global Data Science Platform Market which would mention How the Covid-19 is affecting the Industry, Market Trends and Potential Opportunities in the COVID-19 Landscape, Covid-19 Impact on Key Regions and Proposal for the Global Data Science Platform Market Players to Combat Covid-19 Impact.

Key Market Features in Global (United States, European Union and China) Data Science Platform Market:

The report highlights Data Science Platform market features, including revenue, weighted average regional price, capacity utilization rate, production rate, gross margins, consumption, import & export, supply & demand, cost bench-marking, market share, CAGR, and gross margin.

Analytical Market Highlights & Approach

The Global (United States, European Union and China) Data Science Platform Market report provides the rigorously studied and evaluated data of the top industry players and their scope in the market by means of several analytical tools. The analytical tools such as Porters five forces analysis, feasibility study, SWOT analysis, and ROI analysis have been practiced reviewing the growth of the key players operating in the market.

List of Best Players profiled in Data Science Platform Market Report;

Google, Inc., Domino Data Lab, IBM Corporation, Datarobot, Inc., Microsoft Corporation, Wolfram, Continuum Analytics, Inc., Dataiku, Bridgei2i Analytics, Feature Labs, Datarpm, Rexer Analytics, Civis Analytics, Sense, Inc., Alteryx, Inc., Rapidminer, Inc., IBM, Snowflake, MeritDirect, Cazena, CBIG Consulting, Loggly, Clairvoyant, Arcadia, Experfy, Datatorrent, Jethro, Tableau, VMware, New Relic, Alation, Tera Data, SAP, Alpine Data Labs, SiSense, Thoughtworks, MuSigma, Cogito, Datameer among others

Key Benefits:

The report provides a qualitative and quantitative analysis of the current Data Science Platform market trends, forecasts, and market size to determine the prevailing opportunities.
Porter’s Five Forces analysis highlights the potency of buyers and suppliers to enable stakeholders to make strategic business decisions and determine the level of competition in the industry.
Top impacting factors & major investment pockets are highlighted in the research.
The major countries in each region are analyzed and their revenue contribution is mentioned.
The market report also provides an understanding of the current position of the market players active in the Data Science Platform industry.

Our analysts monitoring the situation across the globe explains that the market will generate remunerative prospects for producers post COVID-19 crisis. The report aims to provide an additional illustration of the latest scenario, economic slowdown, and COVID-19 impact on the overall industry.)

Key poles of the TOC:

Chapter 1 Global Data Science Platform Market Business Overview
Chapter 2 Major Breakdown by Type
Chapter 3 Major Application Wise Breakdown (Revenue & Volume)
Chapter 4 Manufacture Market Breakdown
Chapter 5 Sales & Estimates Market Study
Chapter 6 Key Manufacturers Production and Sales Market Comparison Breakdown
…………………..
Chapter 8 Manufacturers, Deals and Closings Market Evaluation & Aggressiveness
Chapter 9 Key Companies Breakdown by Overall Market Size & Revenue by Type
…………………….
Chapter 11 Business / Industry Chain (Value & Supply Chain Analysis)
Chapter 12 Conclusions & Appendix

What Businesses Can Hope to Get in Business Intelligence on Data Science Platform Market?

The study insights on the Data Science Platform market growth dynamics and opportunities highlights various key aspects, in which crucial ones are:

Which are the technology and strategic areas that emerging, new entrants, and established players should focus on keep growing in the industry-wide disruptions that COVID-19 has caused?
Which new avenues bear incredible potential during the ongoing COVID-19 lockdown restrictions?
Which policies by governments can give the top stakeholders support their efforts of consolidation?
What new business models are gathering pace among companies to remain agile in post-COVID-era?
Which segments will see a surge in popularity in near future, and what calibrations players need to make to utilize the trend for an elongated period?

About Data Bridge Market Research:

An absolute way to forecast what future holds is to comprehend the trend today!

Data Bridge set forth itself as an unconventional and neoteric Market research and consulting firm with unparalleled level of resilience and integrated approaches. We are determined to unearth the best market opportunities and foster efficient information for your business to thrive in the market. Data Bridge endeavors to provide appropriate solutions to the complex business challenges and initiates an effortless decision-making process. Data bridge is an aftermath of sheer wisdom and experience which was formulated and framed in the year 2015 in Pune.

The post DATA SCIENCE PLATFORM MARKET COMPETITIVE ANALYSIS, MARKET ENTRY STRATEGY, PRICING TRENDS, SUSTAINABILITY TRENDS AND INNOVATION TRENDS appeared first on Artificial Intelligence.

It is incredibly tough for a lot of enterprises to make more informed decisions if the ROI behind data science models is less than the investment put behind it. A survey conducted by Deloitte says that a staggering 67% of executives are not comfortable accessing or using data pipelines from their projects for strategic decision making. Instead, they prefer to stick to their legacy systems than know how to use data analytics. The worst part is- shielding away from data science investments, the most common excuse?

Data analytics does not have enough Return over Investment (ROI). But is that true?

Return on investment is used as performance measurement and evaluation metric. Expressed mathematically as a ratio or a percentage,

 ROI= (Gains – Cost of Investment)/Cost of Investment

There are several ways to calculate the ROI behind a data science project and ML algorithm, but how do you measure a qualitative term in a mathematical equation? So, how does an enterprise deduce the matrices to measure Data Science project success or an ML algorithm accuracy?

Measuring the success behind Data Science and ML algorithms-

• Investment Expenses

How much investment goes behind building an Data Science project and an ML algorithm? Investment expenses are all about forecasting these values as accurately as possible. That’s where the C-Suite brainwork begins. With the understanding of how much has to be spent to launch a new product or extend the functionality of a working system, enterprises can focus on the investment behind data science projects with the potential financial return that justifies the risks. This feasibility study of a data science project is directly connected with the return on investment (ROI) evaluation.

• Opportunity Cost

This simply means the decision-makers must think of how they could have deployed the investments if they didn’t invest it in a data science project.

• Cost of Running an ML Algorithm

Cost of running an ML algorithm means the transaction costs including the time spent on building, testing, and deploying a data science project or an ML model-based solution. Development costs include expenses on IT infrastructure and employee compensation, required man-hours, and maintenance costs.

• Time Estimates

The C-suite must determine how much time an investment behind building a data science project will start to pay off. The factor of time is useful when comparing two or more projects with the same expected ROI to be realized under the same circumstances.

• Inflation vs Returns

While calculating the ROI from Data Science projects and ML algorithms, the enterprise must take the underlying inflation into account to calculate and compare excess return over inflation or actual returns vs nominal returns to get a realistic ROI projection.

Adapting to a Data Science Strategy

According to Gartner, by next year 90% of big companies would hire a Chief Data Officer, a promising role that was almost non-existent a few years ago. Of late, the term C-Suite is gaining a lot of importance – but what does it mean? C-Suite gets its name from a series of titles of top-level executives whose job profile name starts with the letter C, like Chief Executive Officer, Chief Financial Officer, Chief Operating Officer and Chief Information Officer. The recent addition of CDO to the C-Suite has been channelized to develop a holistic strategy towards managing data science projects and unveil new trends and measure the ROI behind a data strategy that the enterprise has attempted to tab for years.

In a nutshell, boosting ROI from data science projects and ML algorithms is crucial for business success but the best way to trigger it would be by getting a bird’s eye view of an organisation’s data science strategy, which will help in predicting success accurately and thus help it to strategize ROI-supported decisions.

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