Big Data Innovation Summit 2014 Toronto: Day 1 Highlights
Tags: Analytics, Big Data, Conference, Customer Experience, Gartner, Healthcare, IE Group, Innovation, Toronto-Canada
Highlights from the presentations by Big Data leaders from TD Bank, Public Health Ontario and First Nations Education Steering Committee on day 1 of Big Data Innovation Summit 2014 in Toronto, Canada.

We provide here a summary of selected talks along with the key takeaways.
Here are highlights from Day 1 (Wednesday, June 4, 2014):

She suggested that instead of "Big Data" we should rather think about "Right Data". Storing vast amounts of data in an extremely efficient manner does not benefit an enterprise if it isn't using that data to generate insights that drive marketing and business decisions. People have a narrow vision of Big Data, thinking of the term merely in the context of IT infrastructure, storage, cost, training, privacy, security, etc. Big Data is a shift in strategy, and not merely a technology.
She quoted the following from Accenture Technology Report - 2013:
"Analytics have always been a challenge, but partially because businesses have often conducted the process in an exploratory fashion—they've collected available data and then analyzed it, rather than assiduously determining what data will aid their business strategy and then ensuring that the right data is collected to analyze."
Citing Gartner reports, she mentioned that Big Data investments are steadily increasing. The most common business problems, for which Big Data is being leveraged, are: enhancing customer experience, improving process efficiency and designing new products / new business models.
The application of Big Data Mindset to Customer Experience needs seamless integration of Customer Experience surveys, enterprise data warehouse and web analytic tools. It is important to understand online behavior and demographics of customers participating in CEI (Customer Experience Index) surveys & Segmentation. Organizations need to develop personas-specific, concrete representations of target users and use this information from strategy to design to testing. Based on prior research and exploratory interviews, determine the key attributes differentiating each persona and the number of personas. In summary, she emphasized that: it is important to develop Big Data Mindset throughout the organization, which requires significant cultural change - transforming the enterprise to become insight-driven.

In this eclectic talk, Jim outlined the challenges and PHO’s plans to address the data management and analytic issues. Canada spends around $211 billion on healthcare, which comprises of 11.2% of its GDP. 30% of this money is spent on hospitals, 16% on Drugs and 15% on Physicians. All these expenses are growing year-over-year by around 2.5%. The current challenges include: communicable and infectious disease surveillance, "determinants of health", "co-infections", chronic diseases - time and combination of factors, genomics and how to change people's attitude and behavior towards personal health. Jim's vision for future includes setting up data federation and using Big Data to measure effectiveness of public health messages.

Big Data Myths:
- Volume is directly proportional to Information: A recent study by Harvard Business School suggests big-data investments fail to perform because most companies cannot handle the information they already have. Thus, instead of going crazy after data collection, companies should rather first focus on making the most of the data they already have.
- Structured data is better than Unstructured data: Generally the term structured data (SD) is applied to databases (DB) and unstructured data (UD) applies to everything else (text, audio, video, images). Though we are technologically better equipped to handle structured data, it is the unstructured data which has far greater magnitude of information.
- Sort it later policy: With storage getting cheaper day-by-day, companies now can just collect the data, and figure out what to do with it later. In fact, with many data sets, the law of diminishing returns starts to apply. In addition, the more data, the longer it takes to sort through it. Sooner than expected, the volume of data transforms from an opportunity to a challenge.
- Prediction Accuracy debate: It's human nature to think that something that is more specific is more accurate. In fact, in a lot of Analytics, prediction is reported without due importance to accuracy, which creates a false confidence, potentially leading to disastrous results. More specific is not always better. It is important to find the balance.
- Data is static – Big Data? We are used to the over-simplistic approach of treating data as a static entity. However, in the Big Data world, any particular data point might be reported differently by different institutions, or at different points in time, or by different people at those institutions. Also, the meaning of data changes over time.
Highlights from day 2.
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