I was watching a movie “Ford Vs Ferrari” over this weekend which depicts one of the most epic rivalries in the world of Automobile. The biopic film shows the quest of a car designer and driver cum engineering specialist who wants to build a world-class racing car for Ford Motors which is capable enough to beat Ferrari at Le Mans, a 24-hour race. To make this happen, Carroll Shelby (car designer) sensitizes Henry Ford-II about multiple bureaucratic red tapes at Ford Motors that they need to leap through to seek reduction in car’s feedback loop.
This reminds me of “Conway’s Law” which when applied to enterprises using various software systems implies – “Organizations are constrained to produce system designs which reflect its own communication style.” Conway’s law provides a particularly important hint towards addressing challenges due to complex data teams and their data pipelines in data analytics systems.
This brings the need of “DataOps” to the fore!
Much more than hype
DataOps is a methodology to automate and optimize challenges in data management to deliver data through its lifecycle. It is based on the similar collaborative culture of Agile and DevOps foundations to balance control and quality with continuous delivery of data insights.
The landscape of data and business intelligence technologies are changing by leaps and bounds. As enterprises try to maximize value from data over a period, they moved from relational databases (RDBMS) to data warehouses (DW) to address growing data volume challenges, then from data warehouse (DW) to data lake (DL) enabled by cloud to address scalability and reliability challenges. Recently some teams have been migrating from data lake (DL) to Delta Lake for turning data lake transactional and to avoid reprocessing.
The evolving architecture patterns and the increasing complexity of all the data V’s (volume, variety, veracity etc.) is impacting the performance and agility of data pipelines. Businesses need more agile, on-demand, quality data to serve newer customer demands and keep innovating continuously to stay relevant in the industry.
Even though DataOps sounds like yet another marketing jargon in heavily crowded list of “*Ops” terms used within software industry, it has its own significance and importance. As stated in Conway’s law, different data teams scattered across organizations in the form of traditional roles (data architects, data analysts, data engineers etc.) as well as newer roles (machine learning (ML) engineers, data scientists, product owners etc.) work in silos. These data stakeholders need to come together to deliver data products and services in an agile, efficient, and collaborative manner.
DataOps addresses this concern along with bringing agility and reducing waste in time-to-value cycle through automation, governance, and monitoring processes. It also enables cross-functional analytics where enterprises can collaborate, replicate, and integrate analytics across their business value chain.
The method to madness!
The common goal of any enterprise data strategy is to utilize data assets effectively to fulfil an organization’s vision. DataOps plays a pivotal role in operationalizing this strategy through the data lifecycle. A set of steps to help you design a holistic DataOps solution design is outlined below:
Assess where you stand:
To design a DataOps solution that guarantees adoption, a detailed study involving enterprise people, process and technology is required. An enterprise-wide survey outlining current maturity through questionnaires is a great beginning to this journey. Undertake a maturity assessment involving key stakeholders within the enterprise covering the following areas:
- Customer journeys and digital touchpoints
- Enterprise data culture
- DevOps lifecycle processes and tools
- Infrastructure and application readiness
- Orchestration platforms and monitoring frameworks
- Skillset availability and roles definition
- Culture and collaboration across teams and functions
Design for outcomes:
A well-designed DataOps solution should have the following capabilities. Ensure these capabilities are catered to in your DataOps solution design.
- Real-Time Data Management – Single view of data, changes captured in real-time to make data available faster
- Seamless Data Ingestion and Integration – Ingest data from any given source database, API, ERP, CRM etc.
- End-to-End Orchestration and Automation – Orchestration of data pipeline and automated data workflow from environment creation, data ingestion, data pipelines, testing to notifications for stakeholders
- 360-Degree Monitoring – Monitoring end-to-end data pipeline using techniques like SPC (statistical process control) to ensure quality code, data, and processes
- Staging Environments and Continuous Testing – Customized Sandbox workspaces for development, testing to higher environments which promotes reuse
- Elevated Security and Governance – Enabling self-service capability with a secure (metadata, storage, data access etc.) as well as governed (auth/permissions, audit, stewardship etc.) solution
Make the right tool choices:
Make tool choices based on your use case, enterprise goals for DataOps and the capabilities you have considered as part of your design. Some tool choice considerations are provided below.
- DataOps solutions can be implemented using COTS (commercial off-the-shelf) tools or can be custom-built. To become a mature DataOps enterprise, it is important to have a repository of components that can be reused.
- There are specialized COTS tools that provide DataOps capabilities only or provide a mix of data management and DataOps capabilities. Some examples of COTS DataOps tools include: DataKitchen, DataOps.live, Zaloni, Unravel and so on.
- There are also several open source or cloud-native tool options that you could combine to implement your DataOps solution. Ex: GitHub, Jenkins, Nifi, Airflow, Spark, Ansible and so on.
In Summary, DataOps also allows enterprises to get better insights into pipeline operations, deliver data faster, bring resilience to handle changes and deliver better business results. DataOps enables organizations to take a step towards excellence in data transformation efforts and helps accelerate their IT modernization journey. It also empowers organizations to embrace change, drive business value through analytics and gain a competitive advantage in the market.
Get started with Infocepts to accelerate your DataOps strategy and implementation across the business value chain.
As seen on PRWEB
MCLEAN, VA. (PRWEB) DECEMBER 15, 2021
Infocepts, a global leader in end-to-end data and analytics solutions, today announced its certification by the Great Place to Work®, India. The Great Place to Work® Certification is recognized by employees and employers worldwide and is considered the standard for identifying and recognizing great workplace culture, employee experience, and leadership. The Institute awarded the recognition to Infocepts by evaluating it on their five dimensions of High-Trust and High-Performance Culture™ – namely Credibility, Respect, Fairness, Pride and Camaraderie.
Infocepts is an aspirational career destination for professionals exclusively focused on data & analytics. “Being an aspirational career destination means being an employer of choice in what we recognize as an incredibly competitive industry. We provide our associates meaningful and challenging work, freedom and autonomy to experiment, visibility into how they can grow, and a safe, flexible, inclusive, and fun working environment.” said Shashank Garg, CEO and Co-founder of Infocepts. “Being recognized as a Great Place to Work is an absolute honor and a testament to all our associates’ smart work. I am extremely proud of the diversity at Infocepts, where all of us, coming from diverse backgrounds, retain our uniqueness and yet come together to help our customers become data-driven and stay modern.”
Infocepts chooses to invest in the development of its associates through its smart learning platform and career-oriented people leadership enabling associates to excel as individual contributors in functional and technical domains or as technical and business leaders influencing teams.
Infocepts highlights for 2021 include:
- Industry recognitions like Gartner’s 2021 Customers’ choice for Data and Analytics Solution providers, 2021 Data Break Through Award and 2021 Excellence in Customer Service Award
- 250 advisory, migration, development, and adoption projects across 40+ enterprise customers improving business outcomes in finance, retail, health, media, and data industries
- On-boarding of 410 lateral associates and 115 fresh graduates
- 23,257 Training Hours and 16 bootcamps across India, Singapore, and the United States
- Focus on enterprise-wide recognition and problem-solving using InfoStars and Kaizen platforms
Infocepts offers reusable data & analytics solutions to drive transformation for its clients. The company engages holistically with customers, going beyond specific tools, taking advantage of its four centers of excellence focused on Business Consulting, Cloud & Data Engineering, Analytics & Data Management, and Service Management across multiple platforms such as AWS, Azure, Snowflake, Databricks, Cloudera, Informatica, Collibra, Tableau, Power BI, and MicroStrategy.
More information about the recognition and employee testimonials are available on: Great Place to Work® – Infocepts profile page
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Most analytics projects fail because operationalization is only addressed as an afterthought. The top barrier to scaling analytics implementations is complexity around integrating the solution within existing enterprise application and integrating the practices across disparate teams supporting them.
In addition, a number of Ops terms are springing up every day, which is leaving the D&A business & IT leaders more confused than ever. This article attempts to define some of the Ops terms relevant for Data and Analytics applications and talks about common enablers and guiding principles to successfully implement the ones relevant for you.
Let’s look at the multiple Ops models below:
Fig 1: D&A Ops Terms
ITOps – The most traditional way of doing the IT operations in any company is “ITOps”. In this, an IT department caters to the infrastructure needs, networking needs and has a Service Desk to serve its internal customers. The department will cover most of the operations like provisioning, maintenance, governance, deployments, audit and security in above three areas. This department will not be responsible for any application-related support. The application development team relies heavily on this team when it comes to any infrastructure-related requirement.
DevOps – With some of the obvious challenges with ITOps , the preferred way of working is “DevOps”. The project teams need to adapt to the processes where there is less dependency on IT team around infrastructure requirements, and the project teams can do the bulk of ops work themselves using a number of tools and technologies. This mainly includes automation of CI-CD pipeline including test validation automation.
BizDevOps – This is a variant of the DevOps model with business representation in DevOps team for closer collaboration and accountability to drive better products, higher efficiency, and early feedbacks.
DevSecOps – This includes adding the security dimension to your DevOps process to ensure the system security and compliance as required for your business. This ensures that security is not an afterthought and it is a responsibility shared by development team as well. This includes infra security, network security and application-level security considerations.
DataOps – It focuses on cultivating data management practices and processes that improve the speed and accuracy of analytics, including data access, quality control, automation, integration, and ultimately, model deployment and management.
CloudOps – With increasing cloud adoption, CloudOps is considered a necessity in an organization. CloudOps mainly covers infrastructure management, platform monitoring and taking predefined corrective actions in an automated way. Key benefits of CloudOps are high availability, agility and scalability.
AIOps – Next level of Ops where AI is used for monitoring and analysing the data within multiple environments and platforms. This combines data points from multiple systems, defines the corelation and generates analytics for further actions, rather than just providing the raw data to Ops team.
NoOps – This is the extreme case of ITOps where there is no dependency on the IT personnel and entire system is automated. Good example of this is serverless computing in cloud platform.
Let us now look at the common guiding principles and enablers which are relevant for all these models as well for any new Ops model which may be defined in the future.
- Agility – The adopted model should help increase the agility of the system to respond to the changes with speed and high quality.
- Continuous improvement – The model should be able to take into consideration the feedback early and learn from the failures to improve the end product.
- Automation – The biggest contributor is the automation of every possible task that is done manually to reduce time, improve quality and increase repeatability.
- Collaboration – The model is successful only when various parts of the organization are working as a singular team towards one goal, and are able to share all knowledge, learnings and feedbacks.
Enablers – There are multiple dimensions on how any model can be enabled using the principles mentioned above.
- People – There is a need to have a team with the right skills and culture, and which is ready to take on the responsibility and accountability to make this work.
- Process – Existing processes need to be optimized as required or new processes should be introduced to improve the overall efficiency of the team, and to improve the quality of the end product.
- Technology – With the focus on automation, technology plays a key role where it enables the team for continuous development and release pipeline. This will cover various aspects of the pipeline like core development, testing, build, release, deployment etc.
Amongst the ones you see above, which Ops model works best for you will depend on the business requirements, application platform and skills availability. It is clear that the Ops model is not optional going forward and one or more DevOps models are required to improve agility, automation, operational excellence and productivity. It requires proper planning, vision, understanding, investments and stakeholders buy-in to achieve desired success with any of the chosen Ops models.
Companies are reimagining their post-pandemic business models and investing significantly in data & analytics to remain relevant. With today’s changing markets, sustainable businesses need a sustainable D&A strategy to compete year-on-year. There are many pathways for successful modernization but finding the right one for your firm requires deliberate decision making.
Learn from the stories of three global enterprises that partnered with Infocepts to improve customer experience, data-enable business, and increase revenue. We share pragmatic takeaways to help you improve your roadmaps, architectural choices, cross-functional collaboration, and talent acquisition strategies.
Our recommendations for accelerating D&A outcomes help organizations to –
- Advance data-driven capabilities
- Build augmented business apps
- Create data products
- Modernize data platforms
- Support data & analytics systems