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Why IBM Digital Analytics (IBM DA)?

When leading a discussion on finding patterns in chaos, Mike Wing, an IBM-er, said in his video, IBM: Seeing In Visible Patterns, that "in a world full of data, it's not about finding a needle in the haystack--it's about understanding the haystack."

Recently, I started following the IBM Wild Ducks Podcast and I listened to how digital analytics is capable of "finding simplicity in the complicated- creating order out of the unpredictable." I could not agree more.

As a Digital Analytics Consultant at IBM, I work mostly on projects that provide implementation training, best practices recommendations, documentation and technical guidance to client technical resources during the initial setup phase. Overall, through IBM Digital Analytics, we strive to enable users to become essential in the digital sense; to attain full data literacy - not just read what data says, but understand its value in a given context, how an insight into a certain visitor behavior can enable you to transform your business, improve on consumer experience metrics, and ultimately improve on your ROI. So, I decided to start contributing to this "up and coming blog", run by an amazing team of individuals, by giving some highpoints on why Digital Analytics is so coveted today.

At IBM, we see customer provided data as a "natural resource" and digital analytics as fuel that drives insights into a wide-range of business questions that enables you to identify what is relevant to you, overcome content fatigue; and empowers you with methodologies, predictive analytics, to objectively leverage your customer provided data to drive and sustain your competitive edge.

How does IBM Digital Analytics work?

IBM DA utilizes "tags" i.e. data requests, to collect site visitor data from different client sites when a 'tagged' site page is rendered by the visitor's browser such as Firefox, Chrome, etc.

The process is completely client-centric - reports show what the end-user decides to track for the purpose of uncovering and understanding the sought-after data insights. In essence, the client has control over data parameter values in the "tag" function calls, which report info on: visited pages, viewed products, visitor activities such as carting, registering or logging on, interacting with specific page elements, etc.

Simply put, data requests with appended parameter values are sent to IBM DA servers where they are loaded in your own data warehouse for subsequent reporting as actionable metrics. In effect, one leverages customer provided data where and when it makes the most sense for the purpose of uncovering analytics-based insights into everyday business questions, soundness of decisions, and calls for action.

Everything repeated, but in a different way

As one business analyst said: "don't treat me like a mushroom - keeping me in the dark by continuously sending me 'useless data', but instead enable me with: one -optimal processing power, two- analytics capabilities, three- skills." IBM digital Analytics covers one and two. For number three, it will take passionate end-users to engage with IBM Consultants to take the full advantage of new models of individualized customer engagement such as the service offered by IBM DA. An endless stream of insights is waiting to be exploited by YOU. How? -just ask the gurus from the IBM DA Client Enablement team. By anticipating your visitor's needs and preferences, you are better equipped to shape and seize opportunities of tomorrow!

Simply said, as IBM CEO, Ginni Rometty, puts it, the natural resource of our generation, data, is "abundant, but worth little unless you refine it" and "the collection and refinement of data will be a basis of competitive advantage for countries and companies." As we create an estimated "2.5 quintillion bytes of data" every day, one must find ways to connect and make sense of data to seize an opportunity to:

• Leverage C2B interactions to improve customer experience metrics

Amplify Day 1 - The Future of Customer Engagement Now

• Drive a process and/or operational process to become smarter

How big data saved skiing from the sharing economy

• Support a decision when to invest or to divest

Data Diaries: How Analytics sold Netflix on House of Cards

• Identify new opportunities for product/service/competitor differentiation

Fashion Forward, a story from Generation D

Carpe Diem & Happy Tracking,

Goran

-IBM Digital Analytics Team

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