Most businesses today have made some investment in data science. Data science projects have a habit of sprouting up team by team within an organisation, resulting in a disconnected approach that is neither scalable nor cost-effective.
Consider how data science is generally implemented in today’s businesses: A line-of-business organisation that wants to make more data-driven choices typically engages a data scientist to develop models tailored to their needs. Another business unit decides to hire a data scientist to construct its R or Python apps after seeing that group’s performance improve. Repeat until every department or division within the company has its own isolated data scientist or data science team.
Furthermore, no two data scientists or teams are likely to use the same set of technologies. The great majority of data science tools and packages are currently open-source and available for download from forums and websites. And, because data science innovation moves at breakneck speed, even a new version of the same package might cause a previously high-performing model to generate incorrect predictions suddenly and without warning.
As a result, the IT group has no visibility into a virtual “Wild West” of different, disconnected data science projects across the firm.
To address this issue, businesses must entrust IT with the creation of scalable, reusable data science environments. This process can be facilitated by training employees to learn Data Science and get the best online course for business analytics .
Currently, each data science team extracts the data they require or desire from the company’s data warehouse, then replicates and manipulates it for their own reasons. They construct their own “shadow” IT infrastructure to meet their computational demands, entirely distinct from the leading IT group. Unfortunately, these shadow IT environments store critical artefacts (such as deployed models) locally, on shared servers, or in the public cloud. This exposes your company to significant risks (such as lost work when major employees leave) and the lack of reproducible evidence for audit proof or proof of compliance.
What exactly is Data Science, and how does it work?
For competent professionals, data science remains one of the most promising and in-demand job paths. Today’s influential data professionals recognise that they must go beyond the traditional abilities of large-scale data analysis, data mining, and programming. Data scientists must master the complete spectrum of the data science life cycle and possess a level of flexibility and awareness to maximise returns at each stage of the process to unearth meaningful intelligence for their organisations.
The data science life cycle is divided into five stages:
- Capture: Includes data entry, data acquisition, data extraction, and signal reception
- Maintain: Includes data cleansing, data warehousing, data processing, data staging, and data architecture.
- Process: Includes data mining, data modelling, classification/clustering, and data summarization.
- Analyse: Includes confirmatory/exploratory, regression, text mining, predictive analysis, and qualitative analysis.
- Communicate: Includes data visualisation, data reporting, decision making, and business intelligence.
Influential data scientists can develop relevant questions, acquire data from various sources, organise the data, translate results into solutions, and present their findings in a way that favourably influences business decisions. Because these talents are required practically in every industry, skilled data scientists are becoming increasingly valuable to businesses.
What is the Role of a Data Scientist?
During the previous decade, data scientists have become crucial assets in almost every company. These professionals are data-driven, well-rounded individuals with superior technical talents who can build complex quantitative algorithms to synthesise and organise large amounts of data to answer questions and drive company strategy. This is complemented with the communication and leadership abilities needed to provide tangible outcomes to a diverse group of stakeholders within an organisation or a company.
Data scientists must be curious and results-oriented, with extensive industry knowledge and communication skills that allow them to communicate highly technical results to non-technical colleagues. They have a strong quantitative foundation in linear algebra and statistics and programming skills, focusing on mining, data warehousing, and modelling, which they use to analyse and build algorithms. All these skills and more can be obtained from the best courses on data science at Greatlearning.
IT and the next phase of Data Science
Let’s move on from the data to the tools that data scientists employ to clean and alter it to build these sophisticated predictive models. Data scientists can choose from an extensive range of mostly open-source tools, and they usually do so freely. Every data scientist or group has a preferred language, tool, or technique, and each data science team develops unique models. This absence of uniformity may appear insignificant, but it means there is no repeatable path to production. When a data science team collaborates with IT to put its model(s) into production, the IT team must constantly reinvent the wheel.
The previously outlined model is neither tenable nor sustainable. Most importantly, it isn’t scalable, which will be critical in the coming decade, when companies will employ hundreds of data scientists and thousands of continually learning and improving models.
IT has a unique opportunity to play a crucial leadership role in developing a scalable data science function. The CIO can tame the “Wild West” by leading the charge to make data science a corporate part rather than departmental expertise by establishing strong governance, standards guidance, repeatable processes, and reproducibility — all of which IT has experience with.
When IT takes the lead, data scientists have the freedom to try out new tools or algorithms while remaining completely governed, allowing their work to be elevated to the level necessary across the enterprise. A clever centralization strategy based on Docker, Kubernetes, and modern microservices, for example, not only saves money for IT but also expands the value that data science teams can offer to the table. Containers’ magic allows data scientists to experiment with their preferred tools without concern of disrupting shared systems. IT can give data scientists the freedom they require while also standardising a few golden containers for use by a larger audience. GPUs and other specific configurations that today’s data research team’s demands can be included in this golden container.
Models and their accompanying data may be tracked throughout their lifecycle, fulfilling compliance and audit needs, thanks to a centrally controlled, collaborative architecture that allows data scientists to collaborate in a uniform, containerized manner. Data science assets, such as underlying data, discussion threads, hardware tiers, software package versions, parameters, outcomes, and the like, can be tracked to enable new data science team members onboard faster. Tracking is essential because when a data scientist departs an organisation, they typically take their institutional knowledge. Bringing data science under the IT umbrella provides the necessary controls to prevent “brain drain” and ensure that anybody may replicate any model at any point in the future.
Conclusion
Furthermore, by setting up systems that allow data scientists to self-serve their own needs, IT can aid expedite data science research. While data scientists have easy access to the data and compute power they require, IT maintains control and can track consumption and assign resources to the teams and projects which need them the most. It’s a win-win situation.
However, CIOs must first take action. The influence of our COVID-era economy currently necessitates the development of new models to deal with rapidly changing operating circumstances. So now is the moment for IT to take command and provide some order to this chaotic atmosphere.
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