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Business Consultant. Works in Digital Legal Management (since 2007), Knowledge Management (KM, since 1999), Contract Lifecycle Management (CLM, since 2006), Digital Contract Management (since 2009), SaaS B2B (since 1998), Signer of the Agile Manifesto (2006). Founder of EuroCloud Italy (2010) and GM of Cloud for Europe (2016). Publishes Internet contents in different subjects since 1996. Owner and founder of the brand B|KM for SaaS B2B production. Co-founder of AltonaSpain (2021), and Noticias Altona (2022), in the audit/compliance sector for the Spanish market. Works in Spain, Italy, The Netherlands. In his private life he’s a writer, art curator, and amateur music composer.
Recent articles
SAP is an enterprise software vendor based in Walldorf, Germany. Its cloud and on-premises enterprise resource planning (ERP) software, including S/4HANA, helps organizations manage their business operations and customer relations. The German multinational also offers a vast array of software solutions tailored to specific facets of the enterprise, including data management, analytics, and supply chain management, as well as solutions aimed at specific industry verticals.
AI is an area of increasing emphasis for SAP, which has a market cap of $295 billion, making it the 14th largest technology vendor in the world.
Here is the latest SAP news and analysis:
SAP systems increasingly targeted by cyber attackers
December 13, 2024: A review of four years of threat intelligence data, presented Friday at Black Hat by Yvan Genuer, a senior security researcher at Onapsis, reports a spike in hacker interest in breaking into SAP ERP systems.
Nearly 25% of SAP ECC customers unsure about their future
December 6, 2024: The future of SAP architectures is hybrid. But according to a survey conducted by the Financials subgroup of the German-speaking SAP User Group (DSAG), where exactly the journey will go has not yet been decided for many organizations.
SAP ups AI factor in its SuccessFactors HCM suite
October 28, 2024: The launch by SAP of new AI capabilities in its SuccessFactors HCM (human capital management) suite Monday is a case of “better late to the party than never,” according to an analyst with Info-Tech Research Group.
Riled by SAP’s AI policy, customers issue list of demands
October 22, 2024: SAP’s strategy of offering AI innovations only in the cloud continues to attract a lot of criticism from its user base. Here’s what SAP customers would like to see happen.
SAP: good figures, but bad mood
October 22, 2024: Employee engagement is suffering from the ongoing restructuring at SAP, although the software company reported good figures and has raised its outlook.
SAP sustainability tracking rollout focuses on data consistency, outlier detection
October 21, 2024: Enterprise CIOs are under increasing pressure from global regulators to rein in sustainability shortfalls due to partner problems. SAP’s pitch is that most enterprise partners are already using SAP, so it’s in an ideal position to collect and distribute partner data.
SAP joins the AI agent era — but not all customers may benefit
October 9, 2024: SAP is expanding its generative AI copilot Joule to include AI agents. Deeply embedded in SAP systems, the company’s agents aim to solve increasingly complex tasks.
SAP launches collaborative AI agents, adds Knowledge Graph
October 8, 2024: SAP’s promised collaboration between its AI copilot, Joule, and other agents will become reality in the fourth quarter of 2024, the company announced at its 2024 TechEd conference Tuesday.
SAP Build gains AI capabilities to help build autonomous agents
October 8, 2024: SAP wants developers to view its Build platform as the one extension solution for all of SAP’s applications, according to Michael Aneling, chief product officer for SAP Business Technology Platform (BTP).
SAP faces probe in the US over alleged price fixing in government contracts
September 24, 2024: German software giant SAP is under investigation by US officials for allegedly conspiring to overcharge the US government for its technology products over the course of a decade. The probe, led by the Department of Justice (DOJ), is focused on whether SAP and its reseller, Carahsoft Technology, colluded to fix prices on sales to the US military and other government entities
SAP CTO to step down after ‘inappropriate behavior’
September 3, 2024: Juergen Mueller is leaving SAP’s executive board, saying his behavior at a company event was incompatible with company values.
SAP partners up to make AI more practical
August 15, 2024: Many companies find it difficult to incorporate AI into their business processes. To change this, SAP wants to work more closely with the appliedAI initiative.
SAP patches critical bugs allowing full system compromise
August 14, 2024: Both the vulnerabilities score above 9 on CVSS and can allow access to sensitive data if not patched immediately.
SAP is restructuring its Executive Board
July 30, 2024: Head of sales Scott Russell and head of marketing Julia White are unexpectedly leaving SAP; White will not be replaced.
SAP restructuring to impact more jobs than expected
July 24, 2024: The restructuring at SAP affects almost a tenth of its workforce. The company estimates the cost of the internal restructuring at around €3 billion.
SAP Q2 results reveal large orgs now firmly on the path to AI
July 24, 2024: It “had a direct impact on our bookings,” company CEO says during second quarter earnings call.
SAP offers AI to all Rise customers — in unknown, varying amounts
July 19, 2024: Joule AI is now available to all Rise with SAP customers, but customers not using SAP Cloud solutions remain out of luck.
SAP security holes raise questions about the rush to AI
July 18, 2024: Cloud security firm Wiz has published a detailed report about SAP security holes, now patched, that raises alarming questions about the secondary role AI efforts are having on cybersecurity defenses.
SAP publishes open source manifesto
June 27, 2024: SAP has made five commitments — make consistent contributions to the community, champion open standards, strive to adopt an open-first approach, nurture open source ecosystems, and adopt a feedback-driven approach.
SAP, Salesforce lead $356 billion enterprise applications market: IDC
June 21, 2024: The software giants were neck-and-neck as the overall enterprise software market grew 12% in 2023, said IDC.
SAP to buy digital adoption specialist WalkMe for $1.5 billion
June 5, 2024: After Signavio and LeanIX, SAP is acquiring the Israeli provider WalkMe to help user companies with their digital transformation.
SAP CEO Christian Klein: Everything we do contains AI
June 5, 2024: SAP CEO Christian Klein kicked off the company’s Sapphire customer conference with the promise of a real productivity boost from AI.
SAP adds more tools for developers on its platform
June 4, 2024: Behind the scenes, SAP is also using AI to extend the capabilities of its Business Technology Platform.
SAP embeds Joule in entire enterprise portfolio, plans integration to other AIs
June 4, 2024: Joule could communicate with other AIs to complete more complex tasks spanning multiple applications, SAP suggests.
SAP AI pact with AWS offers customers more gen AI options
May 29, 2024: SAP wants to work more closely with AWS on AI, complementing existing partnerships with Google and Microsoft.
SAP customers see S/4HANA and AI as top digital transformation drivers
May 20, 2024: With SAP’s end of mainstream maintenance for SAP Business Suite 7 set for 2027, recent findings from the US SAP user group reveal that companies are increasing focus on shifting to S/4HANA and embracing AI.
SAP faces turning point as Hasso Plattner steps down
May 15, 2024: The departure of CEO Hasso Plattner marks the end of the founding era at SAP, and adds further complexities for the German software multinational as it faces ongoing restructuring efforts, among many other challenges to solve.
SAP forecasts clarity in the cloud
May 7, 2024: After customers and user groups that adopted S/4HANA early accused SAP of bait-and-switch tactics, CIO editor-in-chief in Germany Martin Bayer recently sat with Christian Klein, CEO of the multinational software company, to clear the air on cloud reassurance, using gen AI as a migration accelerant, and positive growth for the future.
Deutsche Telekom calls on SAP for IT infrastructure move to Rise
March 22, 2024: Deutsche Telekom will move its SAP infrastructure to Rise with the help of its own IT services subsidiary, T-Systems.
SAP user group: S/4HANA usage is growing, but still in the minority
March 21, 2024: Customers want more information about cloud and AI strategy from the German ERP giant.
SAP and Nvidia expand partnership to aid customers with gen AI
March 18, 2024: SAP is embedding Nvidia’s generative AI foundry service into SAP Datasphere, SAP BTP, RISE with SAP, and SAP’s enterprise applications portfolio to equip customers with greater and more simplified access to the technology.
SAP enhances Datasphere and SAC for AI-driven transformation
March 6, 24: SAP adds new generative AI and data governance features to SAP Datasphere and SAP Analytics Cloud, enabling customers to incorporate non-SAP and unstructured data when creating AI-based planning models and scenarios.
SAP names Philipp Herzig as chief artificial intelligence officer
February 16, 2024: It’s a small promotion and a change of title for one man, and a sign of a larger change in strategic focus for many others at SAP.
SAP 2024 outlook: 5 predictions for customers
February 12, 2024: As SAP continues to position itself as a leader in generative AI and innovative technologies, customers must prepare to navigate new service offerings and an inevitable move to SAP RISE.
SAP has a new succession plan
February 12, 2024: SAP’s board wants to bring former Nokia chairman Pekka Ala-Pietilä on board to succeed founder Hasso Plattner as chairman.
SAP and IBM under scanner of Indian investigative agency for Air India deal
February 5, 2024: Air India failed to adhere to the rules while awarding an ERP contract worth $27 million to SAP India and IBM India.
SAP offers big discount to lure on-prem S/4HANA customers to Rise
January 30, 2024: The restructuring at SAP affects almost a tenth of its workforce. The company estimates the cost of the internal restructuring at around €3 billion.
SAP announces $2.2B restructuring program that’ll impact 8,000 jobs
January 24, 2024: The restructuring program will focus on AI and impact about 7.4% of SAP’s total workforce.
SAP doubles down on cloud-first innovation with executive reshuffle
January 10, 2024: Product engineering head Thomas Saueressig will take on a new role to maximize potential for customers in the cloud, but that’s cold comfort for on-premises users.
SAP pays multi-million fine for bribery
January 11, 2024: With a $220 million fine, SAP is drawing a line under a long-standing investigation by US authorities. The company is alleged to have bribed officials.
SAP faces breakdown in trust over innovation plans
December 5, 2023: The company’s plan to offer future innovations in S/4HANA only to subscribers of its Rise with SAP offering is alienating customers, user conference hears.
SAP unveils tools to help enterprises build their own gen AI apps
November 1, 2023: SAP Build Code suite combines new and existing developer tools, while a foundational AI model trained on anonymized customer data will be available to help automate tasks.
SAP’s new generative AI pricing: Neither transparent nor explainable yet
October 12, 2023: The ERP vendor is adding a new pricing tier to its Rise with SAP offering with an opaque mix of bundled and usage-based pricing for generative AI functionality.
SAP offers faster updates, longer maintenance for S/4HANA in private clouds
October 11, 2023: SAP is offering free migration consultations, more frequent feature releases and two years’ additional maintenance to entice customers to update to S/4HANA Cloud private edition and, ultimately, adopt Rise with SAP.
SAP prepares to add Joule generative AI copilot across its apps
September 26, 2023: Like Salesforce and ServiceNow, SAP is promising to embed an AI copilot throughout its applications, but planning a more gradual roll-out than some competitors. [...]
CIOs are under intense pressure to deliver massive digital transformation initiatives with limited resources under tight time constraints. Boards of directors are placing a high priority on deploying generative AI as fast as possible so their organizations don’t lose competitive advantage. Meanwhile, organizations running SAP ERP platforms have until 2027 to upgrade from ECC and R3 to S/4HANA, when support will end. These are just two examples of the many challenges CIOs have on their plate.
Enter process intelligence, a data-driven approach that’s revolutionizing how CIOs navigate these challenging transformations. By providing a fact-based view of how systems and processes flow within organizations, it enables more informed decision-making at both strategic and tactical levels.
Here’s how it works. The platform uses process mining and augments it with business context to give companies a living digital twin showing the way their business operates. It’s system-agnostic and without bias, which means companies share a common language for understanding and improving how their business runs, connecting them to their processes, their teams to each other, and emerging technologies to their business. Meaning employees and teams can better collaborate to optimize their business within and across processes. Process intelligence can be applied to every process in every industry, allowing processes to scale to the level of your ambition, and drive the results we all know are possible.
Consider a large system migration challenge. Process intelligence helps CIOs tackle the complexity by providing clear visibility into current operations. For instance, a major alcohol distributor uses process intelligence to create detailed heat maps of their requirements across regions and geographies, an analysis that would have been prohibitively expensive and time-consuming using traditional methods. Process intelligence provides a common language between stakeholders by objectively documenting how work flows through the organization, helping managers to make data-driven decisions.
The technology also provides common language for the often-challenging gap between business and IT teams. During an upgrade, when custom code often needs to be retired and bespoke processes need to be standardized, business units may resist change With facts and data, this decision making becomes simpler.
When it comes to generative AI initiatives, many organizations rush in without a proper understanding of their processes and risk implementing a large language model that doesn’t produce the ROI the business expects. Deployments are often extremely complex, involving specialized, high-performance hardware, rollout of use cases, change management and lengthy training cycles to help people adjust to new ways of working. Process intelligence identifies where slowdowns and bottlenecks occur so managers can speed up and, where appropriate, simplify the deployment process.
Real-world success stories demonstrate the technology’s impact. HARMAN, a wholly-owned subsidiary of Samsung Electronics, leveraged process intelligence for business case planning during its transformation journey and currently uses it for fit-gap, custom code analysis and master data cleanup. As a result, accelerating progress towards completing its system migration. Another large consumer products company employed process intelligence to monitor user adoption during hyper care phases of their implementation, quickly identifying and resolving challenges in order execution and fulfillment. The end result? Happier customers.
The benefits of process intelligence extend beyond technical considerations. Project Management Offices (PMOs) find that process intelligence helps define clearer program scope, reducing the risk of scope creep and budget overruns. Systems integrators can bid more accurately on projects and complete them faster when they have detailed process insights at their disposal.
Celonis is the global leader in process mining and process intelligence. Well-known brands such as PepsiCo, Uber, ExxonMobil, Diageo, Mars, Calor Gas, Pfizer and many more employ their platform for system transformation and execute initiatives faster.
To find out how Celonis can help your organization, visit here.
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Olga Forné, CISO de Abertis, ha sido galardonada como CISO del Año en la reciente edición de los CIO 100 Awards Spain 2024. En concreto, el jurado ha valorado la trayectoria y experiencia de la directiva, que han sido claves para introducir la seguridad por defecto en los equipos de innovación y desarrollo de la multinacional española. De este modo, ha logrado anticipar y abordar los riesgos de una forma práctica y posicionar a su organización como referente en un entorno digital cada vez más complejo.
Fundada en 2003, la empresa especializada en gestión de autopistas aprovecha de lleno las capacidades de las nuevas tecnologías, pero no sin descuidar los desafíos que presenta la ciberseguridad, que son muchos, “cada día más”, según explica Forné. “Hay que distinguir dos sectores, el de las corporaciones y el del cibercrimen, que se dedica única y exclusivamente a explotar todas las herramientas posibles y a producir ciberataques. La mayoría de las empresas nos centramos en otras actividades, y dedicamos divisiones pequeñas a la seguridad, por lo que estamos en desventaja”.
En este contexto hay que tirar de “creatividad” y trabajar en varias direcciones: “Muy enfocados en el negocio para que no nos paren la operativa y, por otro lado, intentando mitigar riesgos”, dice. Además, añade, el escenario se está volviendo cada vez más difícil, con nuevas amenazas basadas en potentes tecnologías. Por ello, “tenemos que estudiar muy bien qué vamos a hacer con los recursos que tenemos y, repito, poner creatividad”.
“Nos encontramos en un sector en el que el aprendizaje es diario”
Las claves del éxito
De cara a 2025, y como no podría ser de otra manera, los retos persistirán. “Tenemos que poner mucho acento en la parte de resiliencia, asumir brecha en muchos casos, y en automatizar los aspectos de prevención y detección”, subraya. “ no tenemos gente suficiente, pero aun así hay que aprovechar las nuevas tecnologías tal y como lo hacen desde el cibercrimen, como por ejemplo, la inteligencia artificial (IA)”.
Preguntada por las claves de su éxito, y como extrapolarlas al resteo de la industria, Forné estima que lo principal es “que te apasione tu trabajo porque estamos en un sector de aprendizaje constante, diario”. En este sentido, prosigue, el networking es esencial; “conocer qué hacen otras personas. Muchas veces vamos al corto o medio plazo y no vemos lo que hay fuera, lo que nos puede llegar a pasar. Por último, asegura, “hay que rodearse de personas que te aconsejen de verdad y que tengan mucho espíritu crítico”.
Olga Forné, CISO de Abertis, durante el discurso posterior a la entrega del premio a CISO del Año.
IDG [...]
Discover Financial Services has moved aggressively to the cloud in 2024 with a migration strategy focused on retaining hybrid flexibility and making the most of cloud elasticity.
EVP and CIO Jason Strle, who joined Discover 18 months ago after CIO and CTO roles at Wells Fargo and JPMorgan Chase & Co., has opted to migrate mission-critical workloads using Red Hat OpenShift on AWS. Moving these containerized workloads to AWS offers Discover greater flexibility and agility to handle the spikes and dips of seasonal consumer spending far more efficiently, he says.
Now that much of the migration is complete, the benefits of cloud elasticity have “paid off,” Strle says.
Discover’s implementation is unique in that it operates its OpenShift platform in AWS virtual private clouds (VPC) on an AWS multi-tenant public cloud infrastructure, and with this approach, OpenShift allows for abstraction to the cloud, explains Ed Calusinski, Discover’s VP of enterprise architecture and technology strategy.
For many years, the Riverwood, Ill.-based finserv hosted workloads on a cloud platform within its own data centers. The OpenShift hybrid approach gives Discover the choice to run workloads on private or public clouds, enabling it to better manage and move workloads to multiple clouds and prevent vendor lock-in.
“More workloads were moved in the first six months of this year than in all the years before, by far, orders of magnitude more,” Strle says. “Due to the elasticity of the environment, we were able to handle circumstances such as big surges, and that’s very important to us because of the way we do marketing and campaigns and different ways people interact with our rewards. That can lead to very spiky consumer behavior, and we can dynamically grow our capacity on public clouds.”
The container-based approach also provides Discover with connectivity to on-prem systems and a gateway that allows access to Discover’s core SaaS vendors — ServiceNow and Workday — as well as integration with external vendors, says Strle, who is also considering alternative container-based architectures as cloud options expand.
Banking on hybrid cloud
Discover’s decision to take a container-based approach as early as 2018 reflects the hybrid approach many consumer financial services have adopted to have maximum control over their workloads. For example, by leveraging OpenShift, Discover and other enterprises can achieve portability across AWS, Microsoft Azure, Google Cloud Platform, and IBM Cloud.
But introducing a container-based approach to cloud computing can introduce complexities and challenges, analysts note. Still the openness and capabilities outweigh the risks for those using OpenShift for AWS, says Sid Nag, VP of cloud, edge and AI infrastructure at Gartner.
“They’re using AWS for basic compute services but not for upper-layer compute services,” Nag explains. “They want to have the ability to run OpenShift anywhere — on the public cloud, on premise, or in a private cloud and they can move workloads around across different hybrid environments.”
Gartner predicts 90% of enterprises will adopt a hybrid cloud approach through 2027. The research firm notes that one major challenge all enterprises face in deploying generative AI will be data synchronization across the hybrid cloud environment.
Gearing up for generative AI
In terms of gen AI, Strle and his teams are exploring the potential long-term benefits, beginning with the company’s use of Microsoft’s Copilot for Office and for GitHub.
But Discover is taking a measured approach to the technology, with a centralized AI governance function within the company responsible for evaluating risk management around developing gen AI solutions, Strle says.
Another part of the organization that oversees data and decision analytics, dubbed DNA, is experimenting with Google’s Vertex gen AI platform for possible contact center use cases, he adds. Some Vertex capabilities are in production and the “ecosystem approach” to managing generative AI solutions as opposed to “stitching together a bunch of different AI tools” is the current gameplan, the CIO says.
“We are intentional about allowing some organic exploration of gen AI capabilities,” Strle says, emphasizing that Discover is not yet exposing customers to gen AI capabilities.
The financial services company is also evaluating open-source models based on Meta’s Llama and is considering more advanced gen AI models that make decisions autonomously — but Discover is not in embracing agentic AI yet.
“We are still focused on that ‘human in the loop’ with our deployment because we still have to manage all the risks and compliance associated with these solutions,” Strle says of the current gen AI models, which assist employees with internal tasks or validate and double-check human activity to eliminate errors.
Initially, Discover’s foray into GenAI will be limited to large language models (LLMs) performing document summarization and possibly supporting customer agents but there will be nothing directly customer-facing for the foreseeable future.
“We’re not going to go there,” the CIO says. “Anything that could potentially be making an important decision for the customer or could cause harm or confusion, those are things in the ‘Do Not Touch’ category.”
But in this era of speedy transformation, Strle won’t count anything out. “I’m not seeing an imminent opportunity, but I know that could change quickly so we’re not closing the door on anything,” the CIO says.
The finserv AI playbook
That approach appears to be a common one among the larger financial services players.
In a recent interview with CIO.com, Gill Haus, Chase CIO at JPMorgan Chase, said he is evaluating use of generative AI to improve internal operations, the contact center, and Chase’s travel business, with some gen AI use cases in production. But he will not deploy the technology in customer-facing applications until it is battle-tested and errors such as hallucinations are gone.
Like Discover, Chase has embarked on a major digital transformation, including the development of a new deposit platform, as well as a modernization of its legacy applications into microservices deployed on private clouds and on AWS and other public cloud providers.
“We will be doing use case-based approach,” Haus said. “It’s not going to be geared for a particular line of business. It’s geared for solving a type of problem or action.”
Their cautious approach to cloud and generative AI is typical for consumer lenders, one analyst says.
“While these companies continue to operate a significant number of financial systems in on-premises datacenters, they have been adopting cloud services for customer-facing websites and mobile apps,” says Dave McCarthy, VP of cloud and edge services at IDC.
“The excitement of implementing gen AI capabilities is tempered by the fact that much of this technology is new and unproven this causes risk-averse companies in financial services to take a cautious approach,” McCarthy says. “Most companies start by experimenting with gen AI to improve internal process before adding customer-facing features.” [...]
What is data science?
Data science is a method to glean insights from structured and unstructured data using approaches ranging from statistical analysis to machine learning (ML). For most organizations, it’s employed to transform data into value in the form of improved revenue, reduced costs, business agility, improved customer experience, developing new products, and so on. In short, data science gives the data collected by an organization a purpose.
Data science vs. data analytics
While closely related, data analytics is a component of data science, used to understand what an organization’s data looks like. Data science takes the output of analytics to solve problems. Data scientists say that investigating something with data is simply analysis, so data science takes analysis a step further to explain and solve problems. Another difference between data analytics and data science is timescale. Data analytics describes the current state of reality, whereas data science uses that data to predict and understand the future.
The benefits of data science
The business value of data science depends on organizational needs. Data science could help an organization build tools to predict hardware failures, enabling the organization to perform maintenance and prevent unplanned downtime. It could also help predict what to put on supermarket shelves, or how popular a product will be based on its attributes.
For further insight into the business value of data science, see The unexpected benefits of data analytics and Demystifying the dark science of data analytics.
Data science jobs
While the number of data science degree programs are increasing at a rapid clip, they aren’t necessarily what organizations look for when seeking data scientists. Candidates with a statistics background are popular, especially if they can demonstrate they know whether they’re looking at real results, have domain knowledge to put results in context, and have communication skills that allow them to convey results to business users.
Many organizations look for candidates with PhDs, especially in physics, math, computer science, economics, or even social science. A PhD proves a candidate is capable of doing deep research on a topic and disseminating information to others.
Some of the best data scientists or leaders in data science groups have untraditional backgrounds, even ones with little formal computer training. In many cases, the key is an ability to look at something from a unconventional perspective and understand it.
For further information about data scientist skills, see What is a data scientist? A key data analytics role and a lucrative career, and Essential skills and traits of elite data scientists.
Data science salaries
Here are some of the most popular job titles related to data science and the average salary for each position, according to the most recent data from Indeed:
Analytics manager: $80,000-$176,000
Business intelligence analyst: $56,000-$147,000
Data analyst: $50,000-$128,000
Data architect: $67,000-$173,000
Data engineer: $83,000-$195,000
Data scientist: $76,000-$195,000
Research analyst: $41,000-$134,000
Statistician: $50,000-$143,000
Data science degrees
According to Fortune, these are the top graduate degree programs in data science:
University of California, Berkeley
University of Illinois at Urbana-Champaign
Marshall University
Bay Path University
University of Texas, Austin
University of Missouri, Columbia
Texas Tech University
University of Chicago
University of California, Riverside
Clemson University
Data science training and bootcamps
Given the current shortage of data science talent, many organizations are building out programs to develop internal data science talent.
Bootcamps are another fast-growing avenue for training workers to take on data science roles, and for more details on data science bootcamps, see 15 best data science bootcamps for boosting your career.
Data science certifications
Organizations need data scientists and analysts with expertise in techniques to analyze data. They also need big data architects to translate requirements into systems, data engineers to build and maintain data pipelines, developers who know their way around Hadoop clusters and other technologies, and system administrators and managers to tie everything together. Certifications are one way for candidates to show they have the right skillset. Some of the top data science certifications include:
Certified Analytics Professional (CAP)
Cloudera Data Platform Generalist Certification
Data Science Council of America (DASCA) Senior Data Scientist (SDS)
Data Science Council of America (DASCA) Principal Data Scientist (PDS)
IBM Data Science Professional Certificate
Microsoft Certified: Azure AI Fundamentals
Microsoft Certified: Azure Data Scientist Associate
Open Certified Data Scientist (Open CDS)
SAS Certified Professional: AI and Machine Learning
SAS Certified Advanced Analytics Professional
SAS Certified Data Scientist
Tensorflow Developer Certificate
For more information about big data and data analytics certifications, see The top 9 data analytics certifications, and 12 data science certifications that will pay off.
Data science teams
Data science is generally a team discipline, and data scientists are the core of most data science teams. But moving from data to analysis to production value requires a range of skills and roles. For example, data analysts should be on board to investigate the data before presenting it to the team and to maintain data models. Data engineers are necessary to build data pipelines to enrich data sets and make the data available to the rest of the company.
For further insight into building data science teams, see How to assemble a highly effective analytics team and The secrets of highly successful data analytics teams.
Data science goals and deliverables
The goal of data science is to construct the means to extract business-focused insights from data, and ultimately optimize business processes or provide decision support. This requires an understanding of how value and information flows in a business, and the ability to use that understanding to identify business opportunities. While that may involve one-off projects, data science teams more typically seek to identify key data assets that can be turned into data pipelines that feed maintainable tools and solutions. Examples include credit card fraud monitoring solutions used by banks, or tools used to optimize the placement of wind turbines in wind farms.
Incrementally, presentations that communicate what the team is up to are also important deliverables.
Data science processes
Production engineering teams work on sprint cycles, with projected timelines. That’s often difficult for data science teams to do because a lot of time upfront can be spent just determining whether a project is feasible. Data must be collected and cleaned, and then the team must determine whether it can answer the question efficiently.
Data science ideally should follow the scientific method, though that’s not always the case, or even feasible. Real science takes time: You spend a little bit confirming your hypothesis and then a lot trying to disprove yourself. In business, time-to-answer is important. As a result, data science can often mean going with the good enough answer rather than the best answer. The danger, though, is results can fall victim to confirmation bias or overfitting.
According to computer science portal GeeksforGeeks, a typical data science process includes the following steps:
Define the problem and create a project charter. A data science project charter outlines the objectives, resources, deliverables, and timeline to ensure all stakeholders are aligned.
Retrieve data. Data relevant to the project could be stored in databases, data warehouses, or data lakes. Accessing that data may require navigating the organization’s policies and requesting permissions.
Employ data cleansing, integration, and transformation. Data cleansing removes errors, inconsistencies, and outliers in the data. Integration combines datasets from various sources. Transformation prepares the data for modeling.
Enact exploratory data analysis (EDA). This step uses graphical techniques like scatter plots, histograms, and box plots to visualize data and identify trends. This step helps in the selection of the correct modeling techniques for the project.
Build models. This step involves building ML or deep learning models to make predictions or classifications based on the data.
Present findings and deploy models. After completing the analysis, this step involves presenting the results to stakeholders and deploying models into production systems to automate decision-making or support ongoing analysis.
Data science tools
Data science teams make use of a wide range of tools, including SQL, Python, R, Java, and a cornucopia of open source projects such as Hive, oozie, and TensorFlow. These tools are used for a variety of data-related tasks, ranging from extracting and cleaning data, to subjecting data to algorithmic analysis via statistical methods or ML. According to the Data Science Council of America, some of the most popular data science tools include:
Python: A versatile programming language that’s a favorite of data scientists. It features extensive libraries for manipulating and analyzing data and implementing ML algorithms, including: NumPy, Pandas, seaborn, and scikit-learn.
R: A language and environment for statistical computing and graphics. R is an integral part of the data science toolkit, useful for data exploration, visualization, and statistical modeling.
JupyterLab: This web-based interactive development environment for notebooks, code, and data offers a flexible interface to configure and arrange workflows in data science and ML.
Excel: Microsoft’s spreadsheet software is perhaps the most extensively used BI tool around. It’s also handy for data scientists, working with smaller datasets.
ChatGPT: This generative pre-trained transformer (GPT) has become a powerful tool for data science tasks that can generate and execute Python code, and produce comprehensive analysis reports. It also features plugins for research, math, statistics, automation, and document review.
TensorFlow and PyTorch: These deep learning frameworks help data scientists develop and deploy ML models in the domain of neural networks. They help data scientists perform complex tasks including image recognition and natural language processing (NLP).
Tableau: Now owned by Salesforce, Tableau is a data visualization tool used to create interactive and shareable dashboards.
Apache Spark: This unified analytics engine is designed to process large-scale data, with support for data cleansing, transformation, model building, and evaluation.
Power BI: Microsoft’s Power BI facilitates data gathering, analysis, and presentation. [...]
