Data Quality: Success with Einstein Analytics for Financial Services

Einstein Analytics for FInancial Services Cloud

So you want to blaze a trail to the summit of sales success. You’ve brought Einstein Analytics for Financial Services to use on the journey. During your climb to the summit, Einstein will help you prioritize the most promising leads and referrals, improve client engagement and reduce churn.

All of this is possible. And much more. But there’s one crucial caveat you need to know, starting with the early steps at the base camp of analytics …

Climbers who want success need to check their equipment. In analytics, data is your foundational tool.

Have you checked your data lately?



Data Quality and Einstein Analytics for Financial Services

Data is the foundation that supports the summit, and everything in between. Too much missing data or incorrect data will result in misleading insights and actions. It will send your sales and marketing teams down the wrong path. You might wind up not too far from where you started.

How to Prepare for Einstein Analytics for Financial Services

To avoid that, we recommend steps to prepare for Einstein Analytics for Financial Services. Each of these steps is discussed in our Success Guides for Salesforce Einstein (download the whole 22-page PDF), which gives you detailed advice:

Full Salesforce adoption. Salesforce should not only be up and running, but you should have full adoption (preferably in Lightning, yes?) and Salesforce should be part of everyday use. Without this in place, investing in Einstein won’t make sense. There won’t be enough data, adoption, or cultural readiness.

Clean your data. You may discover poor legacy practices, gaps, broken links (for householding or company hierarchy), and obsolete data. You might find a lack of established processes that are critical to have in place. Data correction, transformation and enrichment have to be done in order to produce reliable segmentation, models and reports.

Do needed process improvements. Are you trying to fix a broken process with Einstein? If so, heed the warning that Einstein will not fix process problems. It may amplify them. Instead change the processes first. You’ll be more likely to see results with Einstein, and you’ll see them faster.

9-Point Checklist for Einstein Analytics for Financial Services

In our Implementation Guide for Einstein, we share 9 key steps for success:

  1. Create the core team
  2. Gather business and technical requirements
  3. Design the data architecture
  4. Identify data transformations and processes
  5. Define segmentation and personas
  6. Set up integrations
  7. Build, train and deploy models for each business outcome
  8. Create reports for insights vs. actions
  9. Establish ongoing maintenance program

As you can imagine, there is a lot to say about these steps. Visit the Guide online or download it as a PDF to get all advice for this checklist.

If You Need to Hire Help

If you need help with Einstein Analytics for Financial Services, there are several choices:

  • Do it internally
  • Use a Salesforce systems integrator
  • Engage an Einstein specialist

Doing it yourself. If you implement Einstein Analytics for Financial Services yourself, build a team with representatives from areas of your organization where you need to gather the following: initial requirements, sign-off on key stages, ownership to implement, lessons reported to staff. One risk is that this team might not have the experience, bandwidth and chemistry to make everything needed happen. Beyond initial implementation, refining and improving the initial deployment is a multi-year process.

If you decide to seek an outside partner, definitely still assemble this internal team. This team will help you to quickly absorb the Einstein Analytics into your Financial Services Cloud, reduce friction to change, increase adoption and achieve ROI.

Using a Salesforce systems integrator. For success with Einstein, there should be no lingering issues with your core implementation of Salesforce and Financial Services Cloud. Objects, fields, data, relationships, workflows, triggers, license provisioning, permissions, etc. must be configured properly.

A Salesforce systems integrator (SI) brings technical knowledge and experience to do this for you. An SI can install Einstein Analytics for Financial Services, map fields, set up reports and get you up and running.

However unlike the base implementation of Salesforce and Financial Services Cloud, which is technical, implementing advanced analytics requires a deep understanding of the data life cycle. SIs may not have the capability to solve your data problems. They may not have experience to recommend data best practices for financial services – particularly in regards to data that’s crucial for advanced analytics.

Thus, it’s important to know the role that a Salesforce systems integrator can, and likely cannot, play. Where a systems integrator’s knowledge leaves off, you will need a Salesforce partner with expertise in data, analytics and financial services domain knowledge.

Working with an Einstein analytics specialist. Although a specialist like Valgen is a partner in the Salesforce consulting program along with systems integrators, we differ significantly from SIs in three key ways when it comes to Einstein Analytics for Financial Services:

  1. Roots in analytics. When implementing advanced and predictive analytics, you want a partner that has deep experience in analytics. The best bet for financial services is also an analytics specialist with domain knowledge in retail banking, wealth management and insurance.
  2. Strategic Einstein implementation. An analytic specialist sees Einstein Analytics for Financial Services as a strategic implementation to serve your business goals, not only a technical implementation. They will apply strategy at the root of all steps and decisions of implementation.
  3. Data quality and advanced analytics. The third and important differentiator is that analytic specialists work with data architecture, data quality and data transformation as part of an analytics practice. As illustrated at the beginning of this post, data is foundational to the success of analytics. In contrast, traditional Salesforce systems integrators tend to gloss over poor data quality. They don’t always have the experience and knowledge to realize the crucial nature of the data, and know how to recognize and help you fix the problems. An analytics specialist knows that poor data quality can derail your expectations for accurate and reliable predictions and insights from Einstein Analytics for Financial Services.

If you’re looking for help with Einstein Analytics and Financial Services Cloud, visit our Salesforce Einstein Consulting page for more information about our services.

Data and Einstein Analytics for Financial Services Cloud

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