This blog moving to Medium

I prefer Medium's platform, moving this blog to the following going forward: https://medium.com/@terigrossheim

Considerations for Data Analytics in Financial Services

While financial services organizations have been early adopters of data analytics, they also have unique challenges. The need for compliance in an industry filled with regulation, it’s critical for financial services organizations to possess accurate and timely data. A mistake or decision made on inaccurate data can cost financial services organizations time, money, and their reputation. This post will explore various unique situations and challenges to financial services organizations with data analytics.

Chief Data Officer Trends

Banking and insurance are among the first industries to take on the Chief Data Officer (CDO) role. Like government, these industries have overarching regulatory requirements, risk-management practices, and a practical knowledge of the importance of data in business and reporting to industry regulators. CDOs exist in a world overcome by data, along with regulation and risk, likely functioning in a complex organization. While the role of CDO continues to evolve, there is an ongoing battle between a CDO’s alignment with IT versus business.  Since the use and management of data belong to both business and IT, CDOs should make note that one is not exclusive of the other. It’s truly a team effort.

Auditing

Internal auditors at financial services organizations also benefit from data analytics. Business processes that pose financial, regulatory, contractual, and fraud-related risks are often audited, as a result auditors must be prepared to provide assurance within a dedicated timeframe. While there are dedicated tools for data analysis for auditors, there are a few capabilities that transcend tools and technologies:

• ETL (Extract, Transform, Load) – In order to obtain data from various (or even single) data sources, ETL is a set of functions that obtain data from sources such as an ERP, CRMs, and databases such as SQL Server.
• Data quality ensures the dataset is valid and complete, does not contain inconsistencies, and matches the data dictionary provided by the owner.
• Sampling a set of features that allow for selecting subsets of data based on questions that need to be answered (eg. How many insurance claims were created last month?)
• Query-based analysis via data sketch, histogram, and Benford diagram
• Lastly, reporting and workflow that supports auditors the ability to make decisions and draw conclusions. Traditional reporting, dashboards, and audit trails fall into this area.

Big Data

Financial services organizations have also been early adopters of big data. Big data within financial organizations is also not immune to regulation, security, and privacy, which all continue to be priorities. In some countries, information barriers are required between certain types of businesses in banking and insurance. With big data, organizations need to be aware of deterministic versus probabilistic modeling. For instance, in probabilistic model analysts should be aware of false positives, such as a financial transaction following a certain path that money launderers follow. While the transaction might follow a certain pattern, it might be for a different reason.

Privacy concerns around big data have also been under the microscope in the media in recent months. The credit and insurance organizations possess volumes of data but need to continue compliance with the Fair Credit Reporting Act, where these organizations do have permission to collect personal data. Financial services organizations must continue to be sensitive to these regulations as they move forward with big data initiatives. A recent World Economic Forum reports shows promise in possible future standards, where codes are assigned to an individual’s preference regarding how his/her data will be used.

Disruption within the Financial Industry

Speaking about personal data, there is discussion regarding the financial industry using personal data for new sources of revenue. Personal data bank (PDBs) is a concept where an entity will collect, protect, and monetize personal data and ensure that this data is entrusted to individuals. In theory, PDBs will operate with personal data similar to bank operations with customer money. According to Maverick Research by Gartner, PDBs can potentially create a new market and with the absence of government regulation, first mover advantage can take hold and as a result, have PayPal-like success. 

References:

Big Data Is Opening Doors, but Maybe Too Many. New York Times, March 2013: http://www.nytimes.com/2013/03/24/technology/big-data-and-a-renewed-debate-over-privacy.html

Big Data Analytics Requires An Ethical Code of Conduct. Gartner ID G00256399. November 2013

CIO Advisory: The Chief Data Officer Trend Gains Momentum. Gartner ID G00254672. January 2014

Maverick Research: Put Your Data in the Bank, Get Dividends. Gartner ID G00264262.  September 2014

Technology Overview: Data Analysis for Auditors Can Lower Audit Costs and Detect Fraud. Gartner ID G00247280. April 2013

Lakeside Legacy Foundation - Messaging Plan & Branding

I recently began volunteering with the Arts & Business Council of Chicago as a business consultant. The Business Volunteer for the Arts (BVA) programOn a project-by-project basis, volunteers contribute their professional skills and resources to enhance the management capacity of arts organizations with limited resources. In return, business volunteers enrich their professional and personal lives - by building their human networks, strengthening their business skills and becoming an integral part of the behind-the-scene world of the arts.

Last week, I began working with the Lakeside Legacy Foundation on a messaging plan/branding project. I'm planning to conduct a perceptual mapping study to determine how they can better marketing their existing overall brand, as well as brands underneath the overall brand. Their team recently took us for a tour of the facilities, provided us history of the building, and shed light on some challenges they are currently facing. More to come as the project progresses!

Karaoke & New Product Ethnographic Study

My Analytical Tools for Marketers class is winding down, presentation and take home exam are remaining in the quarter. This is my final assignment for the course, which used ethnographic techniques to create new products. I created a new karaoke machine, read the report here.

Nintendo Wii & Value Curve

I recently wrote another paper for one of my MBA classes at DePaul, Analytical Tools for Marketers. It focused on the Nintendo Wii and feature recommendations using the value curve tool. Read it here.

Take A Craft Beer Survey - Enter to Win a $20 Gift Card from Revolution Brewing

Enter to win one of three $20 gift cards from +Revolution Brewing Brewery for taking a survey on craft beer - it's a project for my marketing class. There are a few screening questions, if you do not meet the criteria for the survey, you may be disqualified.

Guys: http://svy.mk/1sdfOxe Ladies: http://svy.mk/1yAiOsx

Revolution Brewing & Conjoint Analysis

Here's another consulting report from my Analytical Tools for Marketers class at DePaul. In this paper, I use conjoint analysis to increase market share and customer value for a beer product from one of my favorite breweries, Revolution Brewing. Read the report here.

Repositioning of Wendy's & Perceptual Mapping

I recently wrote a paper about repositioning Wendy's in the fast food market. This paper was an assignment for one of my MBA classes, Analytical Tools for Marketers. Perceptual mapping is a tool is an analytical tool for conducting perceived brand analysis in a given industry. The paper can be found here. 

Why Mobile BI?

The consumerization of IT has disrupted several facets of our daily lives, including the use of mobile devices in the workplace. Organizations need to have a mobility strategy in place to stay competitive, which includes mobile business intelligence. According to Boris Evelson, VP and Principal Analyst at Forrester, “Mobility is no longer a ‘nice to have’ - it will (be) the new BI mantra”. Execs and employees alike cannot wait to make decisions until they are in the office, in front of their PCs. Evelson also states that 24% of enterprises already use or are piloting mobile BI applications and 37% are considering mobile BI for near-term implementations.

Is your organization ready to compete with these firms, who are implementing methods to review data in real-time to make critical decisions? 

It seems the majority of use cases for mobile BI are focused on the creation and deployment of dashboards and the mobilization of BI content. Healthcare and transportation industries also report consistent use cases, with the transportation utilizing advanced capabilities such as GPS, for items such as routing and alerts. Many BI platforms offer mobile deployment options without additional tools or development has eased adoption in small and midsize businesses.

Current mobile BI solutions are focused on information consumption. Typically, this means interactive dashboards that enable a level of user engagement not usually seen in traditional BI solutions. According to Joao Tapadinhas, Research Director at Gartner, the most common use cases for mobile BI include:

• BI content mobilization - Execution on mobile devices of content built for desktop consumption

• Management dashboards - Often used to delivery KPIs

• Field workers’ reports - Delivers a narrow scope of information for a limited set of business processes

• Mobile analytics - Delivers information through features such as prebuilt analytic models, broad information navigations, diverse sorting, drilling and filtering possibilities, table/chart/maps manipulation, which are combined to support basic analytic processes

• Mobile BI applications - Creation of software that runs autonomously from any BI platform

Tapadinhas recommends the implementation of a mobile BI initiative must be preceded by a business case and the creation of a strategy, which align with existing BI and mobility strategies, with consideration regarding where and how mobile BI will impact the organization. This is where Mikan Associates can help your team.

Mikan Associates specializes in working with teams to develop and implement a successful business intelligence strategy.  Mikan’s Information Delivery practice can tailor a solution, which takes both existing and new data, and processes it in a way to deliver to mobile devices using data analytics dashboards, data visualization, and reporting tools. While the technology is a vehicle with information delivery, Mikan Associates believes linking impactful data and analytics solution to business objectives.

References

Survey Analysis: BI and Analytics Spending Intentions, 2014. Gartner ID G00261704

Top BI Predictions for 2013 and Beyond. http://blogs.forrester.com/boris_evelson/12-12-12-top_10_bi_predictions_for_2013_and_beyond 

Innovation Insight: Mobile BI Innovation Expands Business Analytics Boundaries. Gartner ID G00232732

Deploying the Right Styles of Mobile BI to Fit the Organization’s Needs. Gartner ID G00234392

Evaluating Org Culture at Apple

For my MGT500 course (Managing for Effective & Ethical Organizational Behavior) this summer, I wrote a research paper on organizational culture at Apple. It can be found here.