How to Get Started with Analytics and Cognitive Computing

Jun 3, 2016 | All Blogs, Big Data and Analytics

For the past several years, the IT industry has been redefined, driven by simultaneous developments in data, analytics, cloud, mobile, social media, and the Internet of Things (IoT). At the same time, business leaders have shifted their reliance upon gut instinct for decision making and instead are making data-driven business decisions. Data science professions have been invented and adopted in support of this transformation, propelling companies into the next business era, where they interact with data to reason, adapt and continuously learn.

Individuals, organizations and industries thrive on insight, it helps them improve how they experience, learn and operate in the world. For businesses, it can also fuel innovation for a competitive edge. You know the importance of analytics, but how do you get started? We recommend that you take the following practical steps:

Step One: Determine what business problem needs to be solved

Before you introduce any solutions or capabilities to your department or company, bring together appropriate company stakeholders to identify a “getting started” project. In this stage, the team will think through the most pressing challenges in the business. What are the desired outcomes? How will new insights, or faster decision-making, and better data discovery and exploration, address your problem solving challenge? Which of the following questions do you want to solve?

  • What has happened? (Descriptive Analytics)
  • What is likely to happen? (Predictive Analytics)
  • What should I do next? (Prescriptive Analytics)
  • What can I learn from what happened? (Cognitive)

Step Two: Define the data, tools and platforms you will need or need to extend

How will the use of cognitive capabilities, extending your analytics investments and employing new sources of extraneous data deliver the desired outcome? Specifically consider:

  • What capabilities are needed for the project?
  • Where does the needed data reside, and what form is it in? (e.g., structured, unstructured, in-house, licensed/procured or publicly available)
  • What type of infrastructure is desired? Hybrid cloud, on premise or off-premise?
  • What data security considerations have to be taken in to account?
  • How will you best align resources to collect, ingest, curate, annotate and build out taxonomies and ontologies?

In addition to evaluating your technological readiness, you also need to evaluate the organization’s cultural readiness and the best way to empower every employee with insight and the ability to take action. In the case of Watson Analytics, that might take a matter of minutes. Make sure the use case concept is shared with appropriate stakeholders. Are the right people informed and on-board? Can they help in thinking through the scope of work as it develops?

Step Three: Build the prototype, seek and incorporate feedback

In this phase, you will bring together the right data from internal and external sources, and determine the right combination of data, analytics and cognitive capabilities. Depending on what cognitive capabilities are used, i.e., whether they are transparent or not, make sure processes are in place to train your cognitive solution so it learns and improves over time.

Step Four: Ensure future expansion and modifications are based on what you’ve learned

One of the most compelling advantages of a cognitive solution that combines cognitive and analytics capabilities is its ability to ingest and accumulate data and new insight from every interaction continuously in real-time and at scale. Make sure your team is continuing to apply lessons learned from project and implementation findings to evolve current projects and inform future projects.

 

By putting a cognitive lens on the use of advanced analytics and access to all available sources of data, Flagship can enable businesses to become data-driven and deliver insights not previously imagined with cognitive solutions.

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Businesses and governments worldwide are being challenged to make sense of data and gather valuable insights from structured and unstructured data that are emerging from a variety of sources such as videos, blogs and social networking sites. To meet growing client demands in today’s data intensive industries, IBM has established the world’s deepest and broadest portfolio of Big Data and Analytics technologies and solutions, spanning services, software, research and hardware.Flagship Solutions Group helps clients tackle these big data challenges in virtually every industry – from public safety to healthcare, retail, automotive, telecommunications, and everything in between.As evidenced by our broad big data solutions portfolio, powered by IBM, consulting services, capabilities around cloud and capabilities around traditional software deliveries, we offer the full spectrum of analytics capabilities organizations need to handle big data and extract value from it — from descriptive, predictive and prescriptive to cognitive, including predictive capabilities that allow users to model once and deploy broadly on both structured and unstructured data. Effectively harnessing unique insights from big data can drive better business outcomes, helping businesses grow, attract and retain customers, improve the intimacy enterprises have with their clients and understand more about customers – how to anticipate their needs and provide more targeted and relevant marketing and services. Learn more by exploring our many content pieces. 

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