The Decision Maker

Budgeting for Credit Union Big Data/Analytics

Posted by Peter Keers, PMP on Nov 3, 2014 4:18:00 PM

big-data-sea

‘Tis the budgeting season and credit unions aiming to kick off a 2015 Big Data and Analytics (BD/A) initiative are all asking the same question: “How much does it cost?”

Finding the “number” means asking and seeking answers to two additional questions.

Question 1 – What is It?

In the spirit of Stephen Covey’s habit #2, “Begin with the End in Mind”, clearly defining the BD/A initiative is essential.  To be successful, the overall objective of the initiative needs to be understood across the organization.

Whatever the objective chosen by the credit union, there are some basic building blocks typically needed to establish a Credit Union Big Data/Analytics capability.

a)      Enterprise Data Warehouse Architecture – this is the physical container for the data.

b)      Data Integration Technology – data needs to get from its source and loaded into the warehouse.

c)       Business Intelligence Software – basic data visualization tools are needed.

d)      Analytics Software (e.g. - SAS, SPSS) – more complex analysis requires additional software.

e)      Data Architect/Report Developer – Building out the central data structures and creating basic reporting require these skill sets. While it is not uncommon to find a technical person with both skills, there is commonly more work in both areas than one person can reasonably handle.

f)       Access to a Data ScientistBD/A efforts quickly evolve and the need for expertise in complex analytics arises faster than most organizations expect.

g)      BD/A Roadmap – While the objective of the BD/A initiative may be well understood, a detailed map that specifies priority and sequence will logically organize this significant undertaking.

h)      Steering Committee – It’s all about the success of the business and business executives bear the responsibility for setting and maintaining the course of BD/A.

i)        Data Quality – A BD/A effort will almost immediately expose data quality issues lurking in source systems. Credit unions often underestimate by several orders of magnitude the costly data quality issues to be dealt with in such projects.

j)        Time – Credit unions starting the BD/A journey usually misjudge the amount of time needed for a successful launch. The journey is not linear. Rather, it is usually an iterative “journey of discovery” in which new information negates previous assumptions and forces the project back several steps.  

 

Question 2 - Build or Buy?

Once the credit union understands the scope of the BD/A initiative, the next question is, “Do I build it myself or buy a vendor solution”?

The advantage of the DIY approach is a customized solution the credit union owns. As attractive as this seems, the disadvantages are huge:

  • Time and cost to build are substantial
  • Ongoing costs to maintain the technology are also substantial
  • Credit unions are financial experts, not data experts
  • Staff attrition is always a risk

Even for a large credit union with sophisticated Information Technology capabilities, the costs of the DIY option can be enormous. Consider the following chart depicting a hypothetical DIY Total Cost of Ownership (TCO) scenario.

Resource Description

2015

2016

2017

2018

2019

Total

Data Architect(Full-Time)

$120,000

$120,000

$120,000

$120,000

$120,000

$600,000

Consulting (Initial Build)

$250,000

$100,000

$0

$0

$0

$350,000

Consulting (Additions/Upgrades)

$0

$0

$60,000

$60,000

$60,000

$180,000

Report Writer (Part-Time)

$80,000

$80,000

$80,000

$80,000

$80,000

$400,000

BD/A Software & Maintenance

$50,000

$9,000

$9,000

$9,000

$9,000

$86,000

Analytics Software (e.g. – Tableau) & Maintenance.

$150,000

$27,000

$27,000

$27,000

$27,000

$258,000

Consulting (Data Scientist)

$50,000

$50,000

$50,000

$50,000

$50,000

$250,000

Hardware

$20,000

$0

$0

$0

$0

$20,000

Total

$720,000

$386,000

$346,000

$346,000

$346,000

$2,144,000

The option of buying a vendor solution has its pros and cons also. The most obvious drawback is the credit union does not own the technology. This can result in a perceived lack of flexibility in cases where the credit union requires changes to tailor the solution to it specific needs.

Nevertheless, the credit union industry is well-suited to gain a lot of value from this option. By leveraging the collaborative nature of credit unions, a vendor solution is a very attractive alternative for a number of reasons.

  • Lower TCO – vendor solutions are standardized and spread across many credit unions
  • Industry expertise – a vendor that is industry -specific knows the special needs of credit unions
  • Access to data integration templates – there is no need to “reinvent the wheel”
  • Access to shared applications – applications used across the industry can be used “off the shelf"
  • Ability to pool data for analysis – a vendor with many credit union clients can offer this capability
  • No additional staff needed – the vendor’s staff is efficiently allocated across multiple clients to provide top-flight expertise at a fraction of the cost of an FTE in each credit union.

 By utilizing Credit Union Service Organizations (CUSOs), the Total Cost of Ownership is drastically different because costs are shared by several credit unions.  Consider the following chart depicting a hypothetical Big Data/Analytics CUSO Total Cost of Ownership (TCO) scenario.

Resource Description 2015 2016 2017 2018 2019 Total
Data Architect (Full-Time) $0 $0 $0 $0 $0 $0
Implementation $20,000 $0 $0 $0 $0 $20,000
Additions/Upgrades $0 $20,000 $20,000 $20,000 $20,000 $80,000
Report Writing Services $10,000 $10,000 $10,000 $10,000 $10,000 $50,000
BD/A Software & Maintenance $30,000 $4,000 $4,000 $4,000 $4,000 $46,000
Analytics Software (e.g. - Tableau) & Maintenance. $40,000 $8,000 $8,000 $8,000 $8,000 $72,000
Consulting (Data Scientist) $15,000 $15,000 $15,000 $15,000 $15,000 $75,000
Hardware  $20,000 $0 $0 $0 $0 $20,000
SQL Server 2008 R2(2 licenses - standard version) $40,000 $0 $0 $0 $0 $40,000
Total  $  175,000  $        57,000  $  57,000  $  57,000  $  57,000  $  403,000

Answering these two questions may take significant effort. However, credit unions putting adequate time and resources into the task will be able to determine a realistic budget number and give their BD/A initiatives a good start on the road to success.

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Topics: Big Data, Data warehouse, Budgeting, Big data/analytics

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