Big data gurus have said that data quality isn’t important for big data. Good enough is good enough. However, business stakeholders still complain about poor data quality. In fact, when Forrester surveyed customer intelligence professionals, the ability to integrate data and manage data quality are the top two factors holding customer intelligence back.
So, do big data gurus have it wrong? Sort of . . .
I had the chance to attend and present at a marketing event put on by MITX last week in Boston that focused on data science for marketing and customer experience. I recommend all data and big data professionals do this. Here is why. How marketers and agencies talk about big data and data science is different than how IT talks about it. This isn’t just a language barrier, it’s a philosophy barrier. Let’s look at this closer:
Data is totals. When IT talks about data, it’s talking of the physical elements stored in systems. When marketing talks about data, it’s referring to the totals and calculation outputs from analysis.
Quality is completeness. At the MITX event, Panera Bread was asked, how do they understand customers that pay cash? This lack of data didn’t hinder analysis. Panera looked at customers in their loyalty program and promotions that paid cash to make assumptions about this segment and their behavior. Analytics was the data quality tool that completed the customer picture.
Data rules are algorithms. When rules are applied to data, these are more aligned to segmentation and status that would be input into personalized customer interaction. Data rules are not about transformation to marketers.
Ari Kaplan is a real moneyball guy. As President of Ariball, he has worked with more than half of all the MLB organizations to evaluate players for maximum return on the baseball club's investment. But, Ari is much more than just a moneyball guy, he is also a computer scientist, a data scientist, and has the business acumen to produce dramatic results for the teams he works with. He is the real deal. Forrester TechnoPolitics caught up with Ari at Predictive Analytics World in Chicago to ask him how Big Data and the role of the data scientist will advance the science of moneyball.
Too little data, too much data, inaccessible data, reports and dashboard that take too long to produce and often aren’t fit for purpose, analytics tools that can only be used by a handful of trained specialists – the list of complaints about business intelligence (BI) delivery is long, and IT is often seen as part of the problem. At the same time, BI has been a top implementation priority for organizations for a number of years now, as firms clearly recognize the value of data and analytics when it comes to improving decisions and outcomes.
So what can you do to make sure that your BI initiative doesn't end up on the scrap heap of failed projects? Seeking answers to this question isn't unique to BI projects — but there is an added sense of urgency in the BI context, given that BI-related endeavors are typically difficult to get off the ground, and there are horror stories aplenty of big-ticket BI investments that haven’t yielded the desired benefit.
In a recent research project, we set out to discover what sets apart successful BI projects from those that struggle. The best practices we identified may seem obvious, but they are what differentiates those whose BI projects fail to meet business needs (or fail altogether) from those whose projects are successful. Overall, it’s about finding the right balance between business and IT when it comes to responsibilities and tasks – neither party can go it alone. The six key best practices are:
· Put the business into business intelligence.
· Be agile, and aim to deliver self-service.
· Establish a solid foundation for your data as well your BI initiative.
I had a conversation recently with Brian Lent, founder, chairman, and CTO of Medio. If you don’t know Brian, he has worked with companies such as Google and Amazon to build and hone their algorithms and is currently taking predictive analytics to mobile engagement. The perspective he brings as a data scientist not only has ramifications for big data analytics, but drastically shifts the paradigm for how we architect our master data and ensure quality.
We discussed big data analytics in the context of behavior and engagement. Think shopping carts and search. At the core, analytics is about the “closed loop.” It is, as Brian says, a rinse and repeat cycle. You gain insight for relevant engagement with a customer, you engage, then you take the results of that engagement and put them back into the analysis.
Sounds simple, but think about what that means for data management. Brian provided two principles:
How is it possible for a local company to defeat global giants like Pepsi, Coca-Cola, and Watsons in your market segment and establish market leadership for more than a decade? The answer is given by Nongfu Spring, a Chinese company in manufacturing and retail industries. In my recent report “Case Study: Technology Innovation Enables Nongfu Spring To Strengthen Market Leadership”, I analyzed the key factors behind their success, and provide related best practice from enterprise architecture perspective. These factors include
Business strategy is enterprise architecture's top priority. EA pros often need to be involved in project-level IT activities to resolve issues and help IT teams put out fires. But it's much more important that architects have a vision, clearly understand the business strategy, and thoroughly consider the appropriate road map that will support it in order to be able to address the root causes of challenges.
Agile infrastructure sets up the foundation for scalable business growth. Infrastructure scalability is the basis of business scalability. Infrastructure experts should consider not only the agility that virtualization and IaaS solutions will provide next-generation infrastructure, but also network-level load balancing among multiple telecom carriers. They should also refine the network topology for enterprise security.
Yesterday Intel had a major press and analyst event in San Francisco to talk about their vision for the future of the data center, anchored on what has become in many eyes the virtuous cycle of future infrastructure demand – mobile devices and “the Internet of things” driving cloud resource consumption, which in turn spews out big data which spawns storage and the requirement for yet more computing to analyze it. As usual with these kinds of events from Intel, it was long on serious vision, and strong on strategic positioning but a bit parsimonious on actual future product information with a couple of interesting exceptions.
Content and Core Topics:
No major surprises on the underlying demand-side drivers. The the proliferation of mobile device, the impending Internet of Things and the mountains of big data that they generate will combine to continue to increase demand for cloud-resident infrastructure, particularly servers and storage, both of which present Intel with an opportunity to sell semiconductors. Needless to say, Intel laced their presentations with frequent reminders about who was the king of semiconductor manufacturingJ
Initial business intelligence (BI) ployment efforts are often difficult to predict and may dwarf the investment you made in BI platform software. The effort and costs associated with professional services, whether you use internal staff or hire contractors, depend not only on the complexity of business requirements like metrics, measures, reports, dashboards, and alerts, but also on the number of data sources you are integrating, the complexity of your data integration processes, and logical and physical data modeling. At the very least Forrester recommends considering the following components and their complexity to estimate development, system integration and deployment effort:
I’ve been presenting research on big data and data governance for the past several months where I show a slide of a businesswoman doing a backbend to access data in her laptop. The point I make is that data management has to be hyper-flexible to meet a wider range of analytic and consumption demands than ever before. Translated, you need to cross-train for data management to have cross-fit data.
The challenge is that traditional data management takes a one-size fits-all approach. Data systems are purpose built. If organizations want to reuse a finance warehouse for marketing and sales purposes, it often isn’t a match and a new warehouse is built. If you want to get out of this cycle and go from data couch potato to data athlete, a cross-fit data training program should focus on:
Context first. Understanding how data is used and will provide value drives platform design. Context indicates more than where data is sourced from and where it will be delivered. Context answers: operations or analytics, structured or unstructured, persistent or disposable? These guide decisions around performance, scale, sourcing, cost, and governance.
Data governance zones. Command and control data governance creates a culture of “no” that stifles innovation and can cause the business to go around IT for data needs. The solution is to create policies and processes that give permission as well as mitigate risk. Loosen quality and security standards in projects and scenarios that are in contained environments. Tighten rules and create gates when called for by regulation, where there are ethical conflicts, or when data quality or access exposes the business to significant financial risk.
We recently attended Amdocs' customer event in Singapore. Amdocs is gradually adjusting its strategy to reflect one of the most fundamental changes in the ICT industry today: Increasingly, business line managers, think the marketing or sales officer, are the ones influencing sourcing decisions. Traditional decision-makers, CTOs and CIOs, are no longer the sole ICT decision-makers. Amdocs is addressing this shift by:
Strengthening its customer experience portfolio.Successful telcos will try to regain lost relevance through improved customer experience. Marketing, portfolio product development, and sales are therefore growing in importance for telcos. Amdocs’ integrated customer experience offering, CES 9, provides telcos with a multichannel experience; proactive care; and self-service tools.
Betting big on big data/analytics.Amdocs is leveraging big data/analytics to provide real-time, predictive, and prescriptive insights to telcos about their customers’ behaviour. Communications-industry-specific converged charging and billing solutions as well as other catalogue solutions give Amdocs the opportunity to provide more value to telcos than some of the other players.