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.
Q: How easy is it for managers who are business minded to use analytics tools?
Daniel Pana:Managers should determine the people around them to be more analytical. Most importantly, they follow through and put analytics to work in their decisions and actions.
Analytics are for all of us as we become more data-driven and analytical in our thinking and our work, it is no longer the exclusive province of statisticians and specialists. Leading the way are people Gartner terms as ‘citizen data scientists.’ They can’t do everything the PhDs can, but they are highly analytical – capable of sourcing data, using more sophisticated tools, and communicating to the PhDs what else they need.
Ten years ago, ‘forecasting’ often meant the budget process. And if you said ‘predictive analytics’, people’s eyes might glaze over. Today, managers recognize what analytical models can help us do – anticipate customer responses, predict customer or employee attrition, optimize allocation of resources of all kinds, make smarter decisions faster. Managers may not be able to build the complex models and simulations by themselves, but they can appreciate and capitalize on the outputs of advanced analytics.
Enabling people to access data is one thing. But empowering them to do something intelligent with the data, that’s democratization of analytics. That’s saying, ‘I don’t want you to look just at the total sales by product by region. I want you to determine which combinations are the most profitable. What’s the forecast? What can you change to improve sales?’ That just described elements of descriptive, predictive, and prescriptive analytics. How are we doing, what’s likely to happen, and what should we do to get to the best place possible? At the end of the day, it is all about the analytical attitude that managers have and not about their technical knowledge.
In addition, we can now bring more of the vast amount of unstructured data into the analytical mix. That data has always been there, but now we can work with it. What customers say to our call center representatives, what tech support specialists are writing down as they work, what people are posting on social media – that’s all unstructured text with potential richness of information. Now we can work with it through text analytics, sentiment analysis, and predictive modeling.
Q: What can we expect from the Analytics Experience 2016 in Rome, having in mind the Analytics Experience of the past years for Romania?
Daniel Pana:I am inviting our readers to participate at Analytics experience 2016 in Rome and they will get a 360 view of what European experts’ thoughts are in this respect (For more information and for registration, please access the QR Code at the end of the interview).
As for Romania, for sure we are going to face a higher utilization, a need for collaborative analytics, better and faster data access, streaming, cleaning and enhancing the data helping insight business driven decision process.
Learn more from our past, using historical data and perform better. Large companies from Romania don’t pay enough attention to the quality of the data. Within an organization, acceptable data quality is crucial to operational and transactional processes and to the reliability of business analytics reporting. Data quality is affected by the way data is entered, stored and managed. So, understanding the nature of your data and identifying the relationships between various data objects should be our focus in 2016.
Q: It is believed that behavioral insights cannot be separated from tech innovation. Can you offer an alternative scenario – what would it mean for businesses to separate behavioral insights from tech innovation?
Daniel Pana: Behavioral insights is based on the premise that people are not always the rational, self-interested decision makers described in standard economics textbooks. We know from our own lives that we often fail to do what's best for us, despite our best intentions – be it exercising more, saving money or eating healthily. We can use this understanding of how humans really behave in everyday life to help design and implement better.
Analytics gives a holistic and human point of view of data which connects individual disparate data points in order to tell a story of how, what and why a particular event happened. There’s no separation between being a thought leader and an analytics guru. True innovation and breakthroughs happen in a broader analytics environment. One where you have the freedom to be both business- and technical-minded. Without boundaries, you can create a custom experience that delivers your perfect mix of thought leadership, analytics strategies, learning and connecting.
One more indication that no alternative scenario is possible is IOT penetration into daily life.
Q: How can executive leaders benefit from things related to analytics like forecasting, risk analytics, marketing analytics, fighting fraud?
Daniel Pana: As I said before, every organization should depend on reliable data. Managed well, it will drive revenue, reduce costs and mitigate risk. Managed poorly, it can lose customers, inflate costs and expose businesses to increasing levels of risk. One way to measure the benefits of forecasting is to see how much would have been lost if the forecast was not accurate. Another way to measure them is how much would have been gained (or saved) with improved forecasts.
No matter how your organization prioritizes risk, there are methodologies and best practices to help you establish a risk-aware culture, optimize capital and liquidity, and meet regulatory demands. Risk analytics ensure greater efficiency and transparency, strike the right balance between short- and long-term strategies and confidently address changing regulatory requirements.
Marketing Analytics is about understanding each customer’s path to purchase – no matter how fragmented, optimize each customer journey and engage and delight customers throughout those journeys.
The benefits from analytics are huge regardless the industry or the vertical you go.
Q: From an executive point of view, taking into consideration ‘the new business equation’, what are the most important variables in terms of using the analytics tools in the future? What should we understand about this equation?
Daniel Pana:The most important aspect that will determine how the analytics tools will be used in the future is the culture of the organization. Organizations starting to build a culture driven by curiosity and sustained by analytics will be the ones molding the analytics tools, in the future. The ownership will shift more and more to the business user who will leverage the data and convert it into actionable insights for the benefit of the whole organization. So, it’s the business which now needs to have access to the right information, at the right time and context in order to build a lasting analytics-driven culture.