Q: Is there a right way to do business around the concept of data and analytics?
Daniel Pana: Business Analytics represents the combination of domain knowledge and all forms of analytics in a way that creates analytic applications focused on enabling specific business outcomes, where analytics refers to the skills, technologies, applications and practices (e.g. data & text mining, forecasting, artificial intelligence, statistical and quantitative analysis) for the continuous exploration of data to gain insight that drives business decisions.
With more people actively looking for new answers, discovery becomes widespread in the organization, a bigger part of the mindset and is practised by people in all roles at all levels.
New technologies have given us the opportunity to rethink what data can be used for, how much, and how fast – all in pursuit of more ambitious business goals. The ‘edge’ is where the physical world meets the digital world, where we can record transactions and events. Streaming analytics embraces the edge, grabs the data, and processes it, sometimes right in the end device.
Not long ago we were doing very little with data generated at the edge, and it took a long time to do it. We saved the transaction data and analyzed it later in predictable ways.
In conclusion, there is no right or wrong way in practicing analytics, just commute into analytical matrix and start with what is most suitable for you personally and for your organization.
Q: What is the role of the analytics tool in the new economic environment?
Daniel Pana: In our opinion, BA plays two major roles:
A) Getting the right information for your business from the entire big data volume that one business has.
According to Gartner analyst Svetlana Sicular, ‘Big data is a way to preserve context that is missing in the refined structured data stores — this means a balance between intentionally "dirty" data and data cleaned from unnecessary digital exhaust, sampling or no sampling. A capability to combine multiple data sources creates new expectations for consistent quality; for example, to accurately account for differences in granularity, velocity of changes, lifespan, perishability and dependencies of participating datasets. Convergence of social, mobile, cloud and big data technologies presents new requirements – getting the right information to the consumer quickly, ensuring reliability of external data you don't have control over, validating the relationships among data elements, looking for data synergies and gaps, creating provenance of the data you provide to others, spotting skewed and biased data.’
B) Evolution and organizational transformation
Organizations typically evolve to analytic excellence, either beginning with efficiency goals or addressing growth objectives. The traditional analytic adoption path starts in data-intensive areas like financial management, risk, operations, sales and marketing. As companies move up the maturity curve, they branch out into new functions, such as strategy, product research, customer service, and customer experience.
As the value of analytics grows, organizations are likely to seek a wider range of capabilities – and a more advanced use of existing ones. This dynamic is leading some organizations to create a centralized analytics unit that makes it possible to share analytic resources efficiently and effectively. These centralized enterprise units are the primary source of analytics, providing a home for more advanced skills within the organization. This same dynamic determined the appointment of Chief Analytics Officers (CAO) starting in 2011.
We see more organizations establish enterprise data management functions to coordinate data across business units. We will also see smarter approaches such as information lifecycle management as opposed to the common approach of throwing more hardware at the growing data problem. The information management challenge will grow as millions of next-generation tech-savvy users use feeds and mash-ups to bring data together into usable parts so they can answer their own questions. This gave rise to new challenges, including data security and governance.