Community Chair

Data Warehouse - Evaluation and Implementation

8140419878?profile=RESIZE_710x

 

Data is so important now and we can use it to predict what to do in the business. For making predictions, we collect data in the data warehouse. It is used to collect and analyze business data from different sources. It is the core of the Business Intelligence System that is used for data analyzing and reporting. A data warehouse is a blend of technologies and components that allow the strategic use of data. It is like the electronic storage of a large amount of data that is designed for query and analysis.

Financial institutions including Credit Unions are investing heavily in big data and analytics. One of the terms closely connected to big data and analytics is “Data Warehouse”. But, deciding whether to build or buy a data warehouse is important. It is a powerful tool in the enterprise data management strategy.

To help us understand it in-depth, Naveen Jain (Founder & President, CULytics) and Bob Little (Advisor, CULytics) hosted a workshop. Why we should have a data warehouse, what are the goals, how to implement it, and how to select vendors; the workshop is all about. So, let’s start understating it:

The need of Data Warehouse

When we do some analysis, most of the time goes for gathering data from the system. With the help of data warehousing, the time required for data gathering got compressed. As data is available at the same place, so analysts do not need to spend time on data collection. This increases efficiency. It is one benefit of adopting data warehousing. Keeping a historical trail of data for a business perspective is another benefit of a data warehouse.

In core banking, some applications don’t have data about the change in attributes and elements. With a data warehouse, it has become very easy to maintain such records that are not possible to maintain in the operational system. And, these historical records can be used for certain analysis. So, adopting a data warehouse:

  • Provides ease of data collection
  • Brings trust in the system as records are available
  • Improves data quality and consistency
  • Unburdens the IT department
  • Helps in risk management as access to more data helps in making better and known decisions

Data Analytics Maturity Models for Credit Unions

How to get the value from a business perspective is important. As at the end of the day, if the business can see the value and drive it, then we are successful. When we talk about data warehouse or data analytics strategy, we understand what is important for the business and then we can go down the path for implementing any technology in place. In the overall process of evaluating data warehouse and business intelligence solutions, we need to first look internally and understand what are the things that are important for the business users so that you can make an investment that can return value in less time. It depends upon different dimensions like the people, process, and technology that are being implemented to do data analytics. There are four models:

  1. Ad Hoc: There are fragmented analytics point solutions. Analytics is generally reactive and descriptive of what has happened in the past.
  2. Basic: A data warehouse has been implemented with integrated reporting and dashboards. Analytics is diagnostic and helps determine why things happen.
  3. Managed: There are automated reporting and alerts. There is higher organizational trust in the data. Analytics is beginning to be predictive of what will happen.
  4. Optimized: Analytics has been integrated with business processes. Analytics is prescriptive for what should be done in the business going forward.

Data Warehouse Architecture

There are many data warehouse models available but generally, they work similarly. An ideal Data Warehouse architecture comprises of:

  • Data sources: Source of data could be an operational system, external sources, flat files, etc.
  • Staging Area: Where things are collected together and transformation including normalizing, formatting, etc., happens. ETL
  • Warehouse: Keeps data after cleansing in the form of Summary Data, Meta Data, and Raw Data.
  • Data Marts: It contains a subset of data available in the warehouse. Comprises of Branches, Loans, and Operations
  • Users: Analytics, Reporting, and Mining of data happen at this stage.

An ideal data warehouse should have basic capabilities - provisioning, ingestion, scalability, performance, provenance, integration, administration, and security. These are several models that can be used. Also, we can build something on our own. When we do this, we got to know which ETL technology we are going to use. Which technology for staging server – be it SQL, Hadoop, etc. Similarly, for analytics, reporting, and mining, different technologies can be used. So, when we build our model, it is a very resource-intensive process not just for building it but also for managing it on day to day basis.

Infrastructure Consideration

Data warehouse infrastructure supports a data warehousing environment in combination with technologies. In other words, it a repository of all sorts of data the implementing organization would need in the present and future. But in reality, the features and functions of the data warehouse may vary depending upon the need of the organization. So, organizations have to consider a few parameters before implementing the data warehouse. Try to find out the model requirements- On-premises, Cloud, or Hybrid. Also, work on tools that are required to improve performance and reduce the cost. System availability is crucial, so make sure that it is available for completing the task in time. The total cost of ownership, training, and installation space among others also seek attention.

Before looking out for the vendor solutions, discover the priorities, success metrics, challenges, and opportunities from an organization and business function perspective. Different data warehouse solutions operate differently. Some solutions are with end-to-end integration with data warehouse and business intelligence. Some solutions are in the cloud or on-premise. Each of these has its own set of complexities.

Data Warehouse Implementation

When it comes to a data warehouse implementation, a team of Data Architect, Technical Architect, Database Administrator, Data Integration Developer, Report/ Dashboard Writer, Data Scientist, and Data Analyst is required. It is not mandatory to have all these in house; the operations done by these professionals can be outsourced as a part of managed services. For implementation; resources, timeline, and training are crucial. So, know which resources your Credit Union has to deploy? What will be the contribution from your vendor? Will there be a need for third-party vendors? Work on these to get an estimate of cost and the amount of collaboration required. Keep in mind that all the resources are not required in hand on the first day. As we move forward, based on the requirements, these resources will be required for getting the value from the data that we have. About timeline, evaluate how long will it take for vendor evaluation, when the implementation begins and end. Training is the most important part; as knowing who will need training, and it should be online or in-person can help you perform better and get optimum outcomes.

The key to success is vision and strategy, so make sure you have it. Implementation, integration, and other operations can be outsourced.

Data Warehouse and Business Intelligence

When we talk about the data warehouse, it becomes important to introduce the BI system. Without a data warehouse, the BI system can’t exist and without BI system, we don’t need a data warehouse. These things work closely. Data Warehouse is useful for storing data from multiple resources, whereas Business Intelligence helps in generating business insights. Data Warehouse gives unified data for upstream BI applications; and Data Visualization, Dashboard Creation, & Reporting are done through Business Intelligence. Data Engineers and Back-end Developers are the audiences of Data Warehouse and the audience of business intelligence are Executives, Managers, & Data Analysts.

Some vendors offer solutions for Data Warehouse and Business Intelligence together and individually as per the requirements.

Vendor Considerations

Credit Unions don’t have that many resources; that is where solution providers come into the picture. Some of them will provide end-to-end solutions. When you go out for vendor selection, in addition to features and functionalities, look for:

  • Pricing: Understanding the requirements can help in understanding which pricing model suits the best.
  • Referencability: Don’t forget to analyze the reviews posted by different types of customers before finalizing the vendor.
  • Ecosystem: Make sure that the vendor can integrate with applications and datasets in less time and in a cost-effective manner.
  • Product Roadmap: Look for upcoming features and release cycles.

To start with the data warehouse, try to know the requirements (How to use data, how to drive growth, how to do segmentation) of the business as these are different for different organizations. To be successful in Data warehouse implementation, focus on a few parameters, and make investments if required. These things are executive sponsorship, business engagement & Alignment, planning, communication technical capabilities & skills, and develop good vendor participation.

E-mail me when people leave their comments –

You need to be a member of CULytics Community to add comments!

Join CULytics Community

 

advantedge
altair
ibi
arka
trellance
coopfs
dfa
wherescape
alkami
prismacampaigns
marquis
aiq
totex
cnet
datava
aun
cinch
know

Related Post

 

Ad Unit Settings






Ad Url Settings

 

api-lead-approach
the-amazon-lending-experience
executing-advanced-analytics-do-s-and-don-t
lending-transformation-old-vs-new
data-journey-building-strong-analytical-practices
4-step-iterative-process-building-a-relevant-analytics-practice
significant-measures-towards-new-normal
building-a-strong-analytics-practice-recipe-for-success
data-warehouse-evaluation-and-implementation
explainable-ai-trust-and-transparency
forecasting
top-50-members-using-transactional-website-jun-2020
top-50-cus-with-highest-and-lowest-efficiency-june-2020
importance-of-financial-risk-management
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
secret-sauce-for-long-term-sustainable-business-intelligence-succ
top-pfm-technologies
data-warehouse-and-bi-technologies-opportunities-challenges
top-chatbot-technologies
keys-to-building-an-effective-branch-or-atm-network
top-50-credit-unions-with-highest-and-lowest-accounts-per-member
lowest-and-highest-net-income-per-branch
marketing-holy-grail
top-50-most-and-least-delinquent-credit-unions
modern-marketing-technologies
incremental-low-cost-data-driven-wins
power-of-storytelling
the-cost-of-not-investing-in-data-governance
questions-you-should-ask-before-investing-in-data-warehouse
learnings-from-new-data-based-on-auto-loan-pricing
5-questions-you-need-to-ask-before-investing-in-data-governance
digital-marketing-maturity-models-for-credit-unions
marketing-expense-per-member
top-2-reasons-that-are-holding-credit-unions-back-when-they-are-i
using-data-analytics-to-manage-lending-complexity-while-driving-h
5-reasons-your-credit-union-should-invest-in-data-and-digital-now
top-50-most-and-least-efficient-credit-unions
retail-financial-services-outlook-during-covid-19
use-of-operational-analytics-to-mitigate-the-impact-of-covid-19
top-50-credit-unions-based-on-asset-size
cu-peer-comparison-dashboard
cu-peer-benchmark
all-about-machine-learning-engineering
top-web-design-trends
most-important-social-media-marketing-trends
state-of-digital-marketing-maturing-in-credit-unions
top-kpis-for-email-marketing
data-cloud-and-the-digital-transformation-imperative
digital-trinity-and-you
phases-of-financial-industry
analytics-roundtable-workshop
invitation-to-join-digital-transformation-hub
analytics-in-the-credit-union-business
value-of-member-centricity-and-analytics-in-the-growth-of-cus
all-about-membership-analytics
top-fraud-management-technologies
getting-started-with-your-data-analytics-journey
explore-vizualization-for-credit-unions
investment-in-website-personalization-technologies
data-analytics-supporting-cu-s-first-member-philosophy
loyalty-rewards-and-retention-technologies
member-experience-analytics
channel-analytics-and-its-importance
project-portfolio-management-technologies
investment-in-self-service-data-preparation-technologies
self-service-data-preparation-technologies
new-frontier-in-customer-experience-management
role-of-marketing-analytics-in-credit-unions
important-aspects-of-consumer-lending-analytics
kpis-on-website-analytics
journey-towards-bank-less-banking
investment-in-crm-technologies
top-omni-channel-vendors
conversational-banking-solutions
/top-kpis-for-chief-information-officer
mistakes-to-avoid-when-implementing-a-omnichannel-member
top-things-to-consider-when-building-dashboards
making-digital-marketing-more-agile-through-tag-managers
cecl-solution-providers
mistakes-to-avoid-while-implementing-marketing-automation
p2p-payment-integrated-solutions
kpis-for-social-media-tracking
kpis-for-human-resources-management
investment-in-fintechs-should-or-should-not
top-kpis-for-online-banking
investment-in-marketing-automation-technologies
investment-in-e-signature-technologies-should-or-should-not
tips-and-tricks-to-a-successful-bi-program
kpis-for-credit-card-business
kpis-for-digital-marketing
kpis-for-consumer-lending
hot-topics-for-credit-union-data-leaders
kpis-for-debt-collections
kpis-for-finance
website-personalization-tools
data-integration-technologies
robotic-process-automation-tools
why-data-analytics-initiatives-fail
electronic-signature-softwares
data-governance-tools-for-credit-unions
digital-and-mobile-banking-technologies
report-inconsistencies-are-frustrating
is-your-culture-ready-for-data-analytics
three-big-data-myths
turning-transaction-data-into-a-goldmine-a-becu-case-study
call-for-presentation-for-2019-credit-union-analytics-summit-is-n
top-10-keys-to-successful-data-analytics-practice
credit-union-chooses-accountscore-for-open-banking-transaction-da
how-much-do-you-spend-to-serve-a-customer
marketing-automation-technologies-for-credit-union
alexa-ask-first-abilene-fcu-for-my-balance
dataweb-content-management-technologies-for-credit-unions
efficiency-ratio
web-analytics-technologies
data-warehousing-software-for-banks
customer-experience-software
the-best-kept-secret-for-credit-union-data-analytics
mark-sievewright-on-technology-trends
naveen-jain-on-credit-union-analytics-summit-2018
why-analytics-doesn-t-make-a-difference-by-gary-angel
cuas2018-harnessing-the-right-data
build-a-financial-phone-assistant-for-your-credit-union-in-3-step
2018-culytics-analytics-challenge-winner
update-from-naveen
error-resolution
benefits-of-conversational-apps
who-are-your-most-valuable-members-part-1
how-alexa-can-help-your-credit-union
top-10-kpis-for-measuring-retail-channel-performance
how-much-is-too-much-personalization
top-10-kpis-for-measuring-contact-center-efficiency
pressure-on-margins-for-auto-loans-indirect-auto-loans-declining
best-business-intelligence-technologies-for-credit-unions
establishing-a-thriving-data-analytics-practice-is-a-journey
educational-presentations-from-the-2017-axfi-conference
modelling-alternatives-for-cecl-a-deep-future-analytics-study
data-analytics-use-cases-for-credit-unions-infographic
data-analytics-opportunities-in-credit-union-business
loan-application-analytics-with-cufx
machine-learning-delivers-great-consumer-experiences
deep-insights-of-credit-union-members-data-with-machine-learning
web-analytics-reporting-tips-for-credit-unions
big-data-strategy-roadmap-our-data-journey
webinar-framework-for-member-focused-decision-making
too-many-regulations-hurt-credit-union-members
digital-marketing-automation-solutions
online-banking-boom
transformation-transactions-to-relationships
top-dispute-management-technologies
2020-retail-trends
future-of-artificial-intelligence
2020-culytics-summit-attendee-dashboard
repositioning-the-role-of-marketing
marketing-automation-a-step-towards-marketing-transformation
strategic-agility
using-data-to-navigate-through-the-new-normal
digital-transformation-bcu
highest-and-lowest-new-loan-balances-per-branch-as-of-jun-2020
-new-members-ratio-as-of-june-2020
cus-with-highest-and-lowest-loan-grants-per-member-june-2020
self-service-data-preparation-technologies
highest-and-lowest-marketing-expense-per-member-june-2020
the-amazon-lending-experience
api-lead-approach
4-step-iterative-process-building-a-relevant-analytics-practice
data-journey-building-strong-analytical-practices
post-election-the-cu-outlook
most-and-least-delinquent-credit-unions-sept-2020
leveraging-ach-data-to-produce-real-outcomes
member-engagement-scores-benefits
member-engagement-key-to-serve-the-best
story-of-james-an-intelligence-transformation
executive-kpis-the-pulse-of-the-organization
untangling-member-journey
onboarding-strategy-to-deliver-success
the-importance-of-digital-technologies
top-interactive-financial-calculators
using-artificial-intelligence-to-improve-your-productivity
organizational-transformation-to-drive-growth
multi-year-journey-through-data-transformation
top-50-cus-with-the-highest-and-lowest-member-per-branch
digital-transformation-lessons-through-the-eyes-of-a-ceo
organizational-readiness-for-digital-transformation
ruthless-prioritization-to-do-more-to-learn-more-and-to-earn-more
performance-measures-for-digital-services
analytical-maturity-journey-towards-growth
less-is-more-the-necessity-of-focus-for-strategic-success
solving-the-crm-mrm-puzzle
insights-driven-messaging-member-and-product-onboarding
performance-measures-for-marketing
data-insights-that-drive-member-product-innovation
solving-the-crm-mrm-puzzle
the-agility-flywheel-a-strategy-that-never-goes-out-of-the-way
artificial-intelligence-as-a-playing-field-for-credit-unions
performance-measures-for-call-centers
top-automl-technologies
performance-measures-for-lending
building-business-case-for-data-analytics
driving-innovation-and-change
data-analyze-decide-and-create
digital-readiness-important-steps-to-achieve
digital-readiness-important-steps-to-achieve
enabling-credit-unions-with-ai
culytics-virtual-summit-2022-a-resounding-success
culytics-virtual-summit-2022-day-1
digital-banking-roundtable
digital-marketing-roundtable
transformative-lessons-from-a-chief-digital-officer
data-analytics-roundtable-mar-11
rewind-2022-culytics-day-key-highlights
data-analytics-team-roles
data-warehouse-development
data-analytics-team-size
is-your-data-analytics-program-not-delivering-results
active-deposit-management-for-profitable-growth
data-modeling
maximize-your-success-with-2023-CULytics-summit
biggest-opportunities-for-credit-unions
should-ceos-attend-the-culytics-summit
the-cost-of-a-wrong-decision
biggest-roadblocks-in-becoming-data-driven
a-journey-for-all-organizational-maturity-levels
maximize-your-data-analytics-checkup
navigating-the-data-analytics-landscape
improving-data-literacy
why-credit-union-leaders-should-invest-in-their-teams
why-credit-unions-should-not-invest-in-building-predictive-models
why-should-measure-the-success-of-data-analytics-program
cost-of-choosing-the-wrong-data-analytics-technology-stack
why-data-analytics-strategy-focus-on-supply-and-demand-side
kpis-to-measure-the-success-of-data-analytics-program
data-analytics-for-credit-union-branch-heads
data-organizing-principles
top-data-warehouse-storage-technologies
discover-the-hidden-truth-behind-watermelon-kpis
unveiling-the-hidden-dangers-of-cobra-effect-on-kpis
are-you-accurately-interpreting-your-kpi
unmasking-biases-a-guide-to-data-analysis-and-kpi-definition
uncover-the-power-of-proxy-kpis
unraveling-the-hidden-impact-of-sampling-bias-in-credit-unions
bi-department-structure
hidden-impact-of-confirmation-bias-in-credit-unions
getting-executive-attention-for-your-data-analytics-program
uncovering-biases-in-data-preprocessing
navigating-missing-data-in-credit-unions
navigating-sampling-bias-in-cu
unleash-the-power-of-real-time-data-use-cases
how-confirmation-bias-impacts-cus
breaking-down-selection-bias-in-credit-unions
unmasking-reporting-bias
elevate-your-cu-with-data-analytics-expertise
understanding-and-tackling-volunteer-bias-in-credit-unions
time-period-bias-in-credit-union
overcoming-biases-in-credit-unions
embracing-the-future-fast-future-fundamentals-program-equips-cred
unlock-growth-and-efficiency-credit-unions-guide-to-generative-ai