How to approach Business Analytics? A guide for better insights out of big data
Business Analytics is a complex and new topic for many corporations. We are often asked by our clients how to approach this topic as best as possible and which building blocks are to be considered. In this article, we would like to give interested parties a brief guide on how to address the topic and which building blocks are to be considered to get better insights out of big data.
Transforming a business requires cultural commitment and the ability to organize your efforts around data. Successfully executing a transformation requires the right strategy, methods, and application of technology. Our approach to Data Strategy is a process that starts with your strategic business objectives and views them together with an analysis of your workflows, data requirements, organizational maturity, and technical capabilities. The result are roadmaps for your organization, focused on achieving near-term business benefits and creating a platform for future innovation.
Data Strategy in Brief:
- Identifies business goals and current capabilities
- Determines future data and capability requirements
- Delivers a prioritized roadmap to achieve value early
- Aligns business and technology stakeholders around technology investment
Why Data Strategy?
Having the right data at the right time creates a business advantage, but getting there can be tricky. Focusing on large-scale plans means the risk of becoming entangled in an extended project that might never deliver value. Yet incremental solutions often add to the problem of fractured and fragile systems, hindering further development.
We often find companies in one or more of these situations:
- you have business opportunities that depend on unlocking value from data
- you have a lot of data but are unsure how to leverage it
- you know big data has potential, and you want to know where to start
- you’re slowed by siloed systems, and want to regain agility by integrating their data
Data Strategy is a business-driven initiative that also involves technology. The winners aren’t the first to install a Hadoop cluster or analyze a petabyte of data; the winners are those who learn to harness data in service of their business objectives. Our experience across many industries has informed a method that delivers results early, while simultaneously developing a platform for the future.
How do we work?
Our experts are embedded within your team, where they collaboratively discover your business needs, decision processes, and available data. We work with your executive and technical stakeholders to explore new data sources and technical capabilities with the potential to create business value, then transform data-driven aspirations into prioritized roadmaps.
Throughout the project, you have the benefit of an experienced team that draws from multiple disciplines, including solutions architecture, engineering, and data science. Our proprietary approach to developing a data strategy can:
- Drive technology capability development through decomposition of your organization’s business objectives into reusable, strategic workloads
- Create actionable views of data gaps that are critical input into data-driven transformation efforts
- Assess your organization’s maturity on the path to becoming a data-driven enterprise—ranging across people, processes, and technology
- Define development horizons and prioritized roadmaps for data capability and technology investment
Data architecture is a business-critical activity, because of the importance of data to remaining competitive. Our extensive experience allows us to assess, recommend, and validate architectures that deliver both near-term value and a platform for continuing development.
We work closely with your business and technical teams to plan a platform for your data, interfaces, and applications, while expanding your capabilities to support new products, services, and key business objectives. Plus, we’re vendor-agnostic, meaning we always make the right choices for your needs.
Architecture Advisory in Brief:
- Identifies your required and future technical capabilities
- Designs extensible views of the target architecture
- Recommends appropriate technologies and tools
Why Architecture Advisory?
Getting data to the right place at the right time is crucial. You also need to get it there at the right cost, on a platform that’s both reliable and adaptable to future requirements. Today’s technology offers unprecedented possibilities, but requires considered application: the purpose of Architecture Advisory is to identify the essential information needed to make an architecture decision that ensures an optimal technology investment for your business needs—present and future.
Clients of Architecture Advisory projects often have one or more of these needs:
- You want to implement well-defined new capabilities, such as recommendation or real-time analytics
- You’re looking for the bridge from your legacy systems to new big data capabilities
- You find that your current data systems are preventing future growth
- You need experienced guidance on the newest breed of databases and analytics
Every company’s data needs are as unique as the company itself. By working closely with you, combining your understanding of key technical and business goals with our experience and understanding, we create architectures that are optimized for delivering results early and often.
How do we work?
Because the data that fuels your business comes from everywhere—inside your company, the cloud, and via partner APIs—data architecture needs to be broad, policy-based, and flexible. Modern data solutions are dramatically different from traditional business applications that operated largely in self-contained silos. Deep real-world experience across our teams allows you to accelerate timelines and reduce risks associated with deploying new technologies.
Our experts work in collaboration with your teams to discover and understand your current infrastructure, and to identify the data and tooling requirements of your growing technical capabilities. We also work with your leadership to understand the future projects that your data platform must support, and to ensure that the final architecture delivers quickly on your business goals.
From our experience designing, developing, and deploying complex systems, we’ve developed a proven platform design methodology and set of reference architectures that:
- Successfully align technology blueprints with business needs
- Provide clarity on how to evolve beyond data silos to a shared platform
- Establish repeatable technical patterns to generate business value from Hadoop, Spark, NoSQL databases, Machine Learning and other technologies
- Assess and recommend appropriate service providers and technology vendors
- Reduce time to deployment and increase operational stability
Every company is unique, and creating a market advantage for each client requires both skill and flexibility. We deploy holistic teams of highly-skilled practitioners at the forefront of data engineering and data science.
- Data Engineering services build out your data infrastructure and applications through prototyping, build, and deployment of data pipelines, reliable platforms, and applications.
- Data Science services bring our deep analytical expertise to bear on your data. Investigating hypotheses to expose and validate analytical narratives, we uncover insights and opportunities, and help your decisions and actions become data-driven.
Our agile methods for data science and data engineering ensure we’re able to remain accountable, responsive, and focused on results.
Agile Build in Brief
- We bring tested data engineering and data science talent to your data projects
- Cross-functional teams are focused, adaptable and effective
- An agile development approach delivers results transparently and responsively
How do we work?
The best way to drive data projects is to deploy skilled, multi-talented teams. You can’t have good data science without good data, and you can’t get good data without good engineering. We’re practiced at bringing teams with the right blend of talent to create robust solutions tuned to meet your business needs. We relish hard problems, and make it a priority to deliver results quickly.
Results for data projects can be probabilistic and unpredictable. At the start, it can often look like there’s an obvious route to the business outcome. When you get started, it’s never that simple. Agile teams do away with rigid planning and go into projects with a creative mindset; they embrace uncertainty instead of shying away from it.
Our agile approach, over a schedule of multiple sprints, gives you regular insight and control in the process and allows us to react together to changing business conditions. The way we work offers you many advantages over traditional consulting practices:
- We stay tightly involved with your teams, and remain accountable for delivering value rapidly
- Our cross-functional teams ensure the solution is fit for the purpose and intelligently constructed
- Surgical-size teams can adapt quickly, with minimal bureaucracy
- Agile is not the absence of intent; our teams prioritize the most valuable, hardest, and most uncertain activities first
- Our teams are backed by access to the full depth and experience of our entire staff
Our data projects use agile methodologies to minimize the risks of innovation, provide delivery transparency to your teams, to fail as fast as possible, and achieve value as quickly as possible.
Our work together follows four stages, from planning through to production.
Plan. We begin with a hypothesis and an evaluation of the data and workloads that support your business objectives. Sprint plans and technology needs are identified.
Prove. We develop prototypes to validate the hypothesis. If results are promising, the team focuses on designing for business insights.
Pilot. Statistical models, data infrastructure, and data feeds are built out. We work with you to educate your team about the technology and decision-making opportunities from the data.
Production. Your systems are scaled to support production workloads while operational management for your solution is transferred to your internal team.
Within each stage, our team works against a schedule of multiple sprints. Each sprint typically runs for two weeks and concludes with deliverables for joint review. This means you’ll have frequent access to intermediate results, and project deliverables can adapt to changing requirements. Each subsequent sprint delivers incremental benefits, and incorporates feedback from stakeholders.
To learn more about our approach to Business Analytics read our other position papers or get into contact with us. We’d love to start a conversation with you about how we can help your organization to reap the value of big data.
Strategy & Transformation Consulting from Munich and Silicon Valley Data Science (SVDS) from Mountain View (USA) have decided to bundle their competencies and resources in the design and implementation of holistic business analytics solutions:
Silicon Valley Data Science (SVDS) is a premier business analytics consulting firm focused on data-driven product development and business transformation. We provide a full spectrum of services: from data strategy and exploration through to prototyping, pilots, and production for your tools and data products. We comprise of world-class data scientists and big data technologists, with a stunning track record of being at the forefront of deploying data platforms and models for America’s largest corporations.
Strategy & Transformation Consulting is an independent strategy and transformation consulting company and helps companies to successfully exploit the opportunities of digitization and to actively shape the necessary changes. We design and implement significant transformations End2End by mastering digital progress. Our deep understanding of business strategy and information technology as well as our extensive experience from many successfully conducted business transformations has helped us to do this.
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Strategy & Transformation Consulting
Silicon Valley Data Science (SVDS)