Interview with Jill Dychè on Data Management in the Era of Big Data

Guest blog: www.biblogg.no

 

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Jill Dyche

Jill Dyche, Vice President of SAS Best Practices, was keynote speaker at SAS Forum Norway in September, and was interviewed by Lars Rinnan 

Jill, you delivered a strong keynote, and the audience was really attentive.
You talked about big data and how to get the c-suite to listen. It sounds almost impossible. How do you get them to listen?

You need to meet executives where they are. In other words: figure out what’s important to them now, and then map big data as the answer.
Here’s an example: A large cable company sees an uptick in customer complaints in its call center. They have to add expensive headcount to the support staff. But they decide to incent customers to use social media interactions to ask for support or lodge complaints.  By adding social media transactions to customer profiles, the cable company can not only monitor valuable customers who may be at-risk, it can also “score” its brand reputation based on text analytics of social media interactions. They understand that over half of customer feedback comments are actually installation questions and not complaints. They develop customer support videos and post them on YouTube. Both questions and complaints are reduced, and support staff can be redeployed to cross-selling functions.

Data governance is essential, but how do you get the CXO interested?

Find the problem the CXO needs to solve, and explain how data enables the solution. Many senior executives don’t make the link between a business need and data. If you “deconstruct” the business problem into the data necessary to solve it, you can see the lights go on with executives.


How would you start a data governance process at a large company who has no clue of data governance?
We recommend starting with what I call a “small, controlled project.” Take a business problem, scope it down to a level where data can enable it quickly, and then implement a well-bounded process around data rules and policies necessary to address it.


How is data governance related to big data?

Big data is like any other data: It requires policy making and oversight. In that respect big data should be beholden to larger rules. For instance, data from sensors or devices that may be streaming into your company should be handled in a different way. Is it sensitive? Is it defined? Is it targeted to be consumed by a department? An individual? A machine? All of these factors, and others, should inform the policies around that data. And that’s data governance.

In your experience, are businesses giving data governance enough attention in terms of resources, technology and funding?

Only after they feel enough pain. Very few companies new to data governance actually say, “Hey! Let’s make sure we factor data governance into this new initiative.” Most have to experience the pain of not having the right data for the right business purpose. Go back to the days of Customer Relationship Management and remember how everyone thought they could just plug in a new tool? The validity of the data is directly proportional to the value of the resulting application. Data can no longer be considered an afterthought.

Thanks you for sharing these valuable insights with the readers of biblogg, Jill!

Infographic explains Lifetime Value of a Customer

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Source: How To Calculate Lifetime Value

Nokia flop

Bankers call for advanced analytics

Technology officials in banking industry are deeply interested in the future of business intelligence, specifically predictive analytics processes that can analyze customer behavior. A recent Computing report found that financial officials are able to draw deeper analysis than retailers. Both bankers and store owners are interested in creating conditions that could leave customers feeling free to spend, with banks eager to drive customer dollars to their own line of payment cards.


Targeted offers

As Computing pointed out, banks have access to an important and unique data source for analytics - transaction data from customers' credit cards. Each use of a credit card contains a wealth of information - where it was used, what type of merchant made the sale. Companies can combine these data points to create a picture of customer interests and allow them to create an environment the encourages further spending and incentives that cardholders will want.
"The data is broader than a retailer would get, so it can go very deep and build meaningful profiles of customers. They can then ask, 'Six months ago, this individual was shopping at John Lewis and now they're shopping in Primark. What does that tell me?'" analytics officer Andrew Jennings told the source. "Banks are not very good at this, but the competitive environment is driving them towards [being good at it]. That's what we're seeing today."
According to Computing, Jennings also stated that while banks have depth of data that cannot be matched by individual merchants, the stores are more experienced actually creating analytics models. He mentioned that there is room for alliances between stores and card providers. Banks can agree to give retailers payments for each transaction placed on that institution's payment cards. Financial institutions can also create programs that give rewards directly to customers if they spend at certain allied merchants.



Unique skillsets

TechTarget recently examined efforts by companies to take predictive information from their data. The source consulted with strategic analytics expert Jennifer Golec, who described the ideal analyst's role as threefold - programmer, data scientist and storyteller. They must have programming know-how to deal with the complex and large data sets needed to make a predictive model. The data science will come in handy when developing processes that employ multiple variables. The storytelling flair will help analytics teams explain their findings in clear, business-focused terms to the rest of the company.

P.S.

Soon is comming post about a Business Intelligence solution focusing bank customers and their behaviour. I applied at the DnBNOR innovation price, but was ignored in 2009, maybe because BI analytics was not that actual then. STAY TUNED!

BI for Customers

BI for everyone, does it sound familiar!

It is a fact that Business Intelligence was dedicated to big companies, enterprises because they have that amount of data to be considered interesting for analytics and BI. Now, Gartner started the idea of Bi for mid-size and small businesses, so they need attention too based on BI surveys. But, have you ever thought for a BI solution in Customer Level, or more detailed do you think you can handle a personal BI solution.
ELA will give you the answer.

ELA solution for Customer Intelligence

What ELA is actually?

Elegant Analytics represents the name of a general BI solution in or group of methodologies in Analytics that adapts to every Business profile. In this case, ELA will provide solution for personal finance and planning of your budget. The name of the product is PFI (Personnal Finance Intelligence). Inspired by the TV Show “Luksusfellen” here in Norway, this BI end-user tool may be a solution for all these who fail to maintain well their own economy and for those who want to perform their economy as well. The purpose of this project is to create a Customer Analytical Cube that would process data for each bank costumer using his/her history for its own benefit and then answer you most important queries that users do against their own data.

This solution will include also benchmarking against an Imaginary subject (Ola Nordman) that can be Min, Max or Avg of the customer’s measures in a certain region, for a period of time, similar age group, sex and income levels.

For having more controle and planning your own economy, will be an extra parameter as Target, so users (bank customers) will put their targets for costs and income a month, quarter or a year ahead and always will be warned when they are about to achieve the amount they targeted.

If you want to read more then follow the link where you can download the full project.