Bluewolf - Data Series

Data is The New Oil — Strategies for a Seamless Salesforce Data Integration

Kitton Epps — Fri, 12 Jun 2015 21:31:39 +0000

This is the fourth blog post in a series on managing data in Salesforce. Subscribe to be notified when a new post is published and check out the other blogs in the series.

With the growth of cloud computing and big data, enterprises are dealing with more data — more types of data — than ever before. There has been a renewed focus on developing a better understanding of the value of this information, as it provides a crucial competitive advantage in today’s business landscape—the more relevant data a company can capture, process, and provide in one integrated system, the more competitive advantage and differentiation that company can generate. In this post, let’s consider some different data integration strategies.

Migration

Data migration is the process of moving data between systems. The process includes determining the scope of the data to be migrated, transforming the data set, and choosing a destination system where the data will be inserted. This is most commonly employed when companies are moving from a legacy system to Salesforce or from a Salesforce production instance to a sandbox instance; when backing up data; or when consolidating salesforce org (read Unify Your Data for a Successful Org Merge for more details on consolidation). For additional details about migration, read 4 Steps to a Seamless Salesforce Data Migration.

One-Way Sync

A one-way sync is when data is moved from a single source system to one or more destinations in an ongoing, near-real time, or real time basis. It is beneficial when data need to be kept up-to-date between systems across time. When compared to an automated migration pattern, this strategy is only for migrating data that has changed. Salesforce-1 Lightning Connect enables real-time data integration via clicks and makes it easy to take outside data and treat it like it is in Salesforce.

Bi-Directional Sync

This process involves merging data from two systems to behave as one while respecting their need to exist as different datasets. In other words, this will capture items that exist either in one or both of the systems and synchronize them. An example use case is a company may want to enable bi-directional data flow from legacy system(s) into Salesforce and vice-versa.

Aggregation

Aggregation is the process of collecting or receiving data from multiple systems and merging it into one. This pattern comes in handy when we want to build an API or a report that furnishes Salesforce and any legacy systems in one aggregated response without the need to store duplicate data in multiple systems.

Correlation

Consider a sales organization. The company wants to synchronize data about accounts and contacts with partners, but only for accounts and contacts that both the company and partners have, but they don’t want to send other data to each other. Correlation strategy is useful in the case where we have two systems that want to share data only if they both have a record representing the same item or person in reality.

Based on the specific business use case, data integration between Salesforce and other systems could leverage any of the above strategies with an integration tool provided by Informatica, Dell Boomi, MuleSoft, etc. However, if custom-built integration is a necessity, Salesforce offers a wide array of Open & Bulk APIs based on industry standards, such as REST & SOAP. The Salesforce Data Integration blog discusses these APIs in depth.

No one can ignore it: data is everywhere, both on-premise and in the cloud. To realize the full value of their data, organizations need to be able to integrate it across the entire enterprise. These integrations can be complex and expensive. In fact, according to a recent survey, 48 percent of CIOs said data integration is their #1 pain point. As trends like cloud computing, BYOD (Bring Your Own Device), and IoT (Internet Of Things) become pervasive, companies need to consider integrating not only structured data, but unstructured, semi-structured, and sensor data. To learn more, contact Bluewolf to connect with one of our IT experts.

References

http://blogs.mulesoft.com/anypoint-templates-database-intro/

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4 Steps to A Seamless Salesforce Data Migration

Bel Gu — Wed, 22 Apr 2015 23:17:01 +0000

This is the third blog post in a series on managing data in Salesforce. Subscribe to be notified when a new post is published and check out the other blogs in the series.

Anyone who has managed IT projects is likely to have seen the detrimental consequences of a data migration gone wrong — underestimated scope, poor execution, or over budget. To mitigate the risk of a bad data migration, these four steps are paramount.

Clearly define and design.

In a large or complex data migration with data from multiple sources and business owners, defining the scope of the project is a crucial first step. Take inventory of all data — where it resides, who owns it — to ensure that no data is forgotten and that users have all the information required to perform daily tasks. Produce useful design documentation during a collaborative requirements gathering phase.

Design a visual ‘Source to Destination Entity Document’ to identify all of the data sources, their respective entities, and where they are mapped to in the new platform. For example, if your customer data is recorded on multiple excel spreadsheets managed by sales, but your order data is held in relational database managed by finance, document this clearly and comprehensively.

Ensure that this migration document is easy to understand and can be reviewed by disparate business units so that section owners can identify missing data entities. For example, marketing can identify ahead of time that their crucial customer profiling database has not been included, to avoid last minute changes to scope. Later in the process, field level mapping documents should be provided, providing greater granularity of scope and transformation.
Analyze and clean all data.

Make sure your data is squeaky clean before you migrate. This is the opportunity to remove duplicate records and redefine your data hierarchy. Clean data is the lifeblood of customer relationship management systems. In order for a CRM system to be successful, customer data should follow your standards, have all the required information, and should be duplicate-free.

Over time, and without clear data governance processes, it is natural for the quality and standardization to deteriorate. This deterioration affects not only the end product of your implementation, but also seriously increases development time and scope of the migration process.
Use an agile approach.

Requirements will probably change and this will impact your data migration strategy. It doesnt matter what technology you are using to complete your migration; it’s important to follow some of the key features of Agile development projects in order to stay ahead of the game. Most importantly:

Daily scrums. Impacting requirements changes to technical solutions is one thing. Knowing about them so late that it requires a re-write of the entire process is another. Ensure that the project team is communicating daily and that everyone is made aware of required changes.

Build migration routines incrementally. Group areas of development into logical chunks. This allows for partial migration, and allows users or stakeholders to get early sight of sample migrated data to identify potential issues.
Reconcile, reconcile, and reconcile some more.

The basic requirement of any migration is to prevent unexpected addition or loss. But how do you know that the project is going to be successful? Reconciliation!

Reconciliation represents one of the most important aspects of any migration project. It effectively audits the technical process to evidence that nothing has been added or lost in transport or during the transformation process. What and how much to reconcile is really down to what type of project you are undertaking. It's important to reconcile the key areas and success metrics of the project, but equally important not to spend unnecessary time reconciling every field. Some of the key things to reconcile:

Currency data. Anything that means money should be totaled and compared between systems.

Record owners. Where systems have the capability to record user ownership of certain records (for instance, sales owning accounts and contacts), these should be reconciled between the systems. This is likely to test manually created mapping, as well as developed code.

Entity row counts. It should go without saying that total row counts should be reconciled.

Grouping. An excellent way to test a migration is to count anything that can be grouped. Whether that be counting the amount of opportunities against your customers, or how many opportunities have a certain products on them.

Lastly, does it all have to match? No. Some data may have been re-modeled, transformed, excluded, or not ported for various reasons. However, your reconciliation should take account of and be explanatory of this.

I hope these tips will help your next data migration endeavour. Check back in two weeks for the next installment of this data blog series, in which I will detail data integration tactics. Or, subscribe to this series and receive blogs right in your inbox.

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4 Strategies to Keep Your Salesforce Data Clean

Bel Gu — Wed, 25 Mar 2015 19:10:00 +0000

This is the second blog post in a series on managing data in Salesforce. Subscribe to be notified when a new post is published and check out the other blogs in the series.

Clean data enables better decision making and improves the effectiveness of marketing and sales. It’s also a huge commitment to keep your data clean — if not monitored properly, your data will become dirty and unvaluable again. So once you’ve achieved clean data, how do you keep it that way? Here are four strategies to keep your Salesforce data clean.

Hold every department responsible.

A cross functional data governance committee is essential to ensure ongoing data quality. All business units should be represented in the council, and collectively own data standards for the company as a whole. According to Bluewolf’s State of Salesforce report, 61% of organisations surveyed have a group dedicated to cloud governance. Data governance is a core responsibility of every governance team.
Constrain data input.

What do we mean by constraining data input? By using standard Salesforce functionality, you can standardize what users put into Salesforce.

Data validation rules, combined with required and unique fields can limit incorrect information. Ensuring that picklist fields and lookups are used in place of 'free text' fields are additional ways to constrain inputs.

A more stringent approach is to create workflow rules that route any newly created customer records to the data governance committee for approval. Only once this approval is granted the records be committed. This process may look burdensome but done well it can be efficient and reduce the burden of data cleanup later on.
Integrate with trusted data sources.

Stopping bad data from getting into Salesforce is only half the battle. A key challenge for many companies is keeping data up to date. Phone numbers, emails, and billing addresses can become outdated surprisingly fast.

Third party data sources that verify addresses and supplement existing data with demographic data like age, gender, and household income can reduce the burden of entering the data into Salesforce and help correct errors.

These data sources do not have to be external. For example, if a customer can manage their own account information online or by calling into customer service, ensure any updated data from the website and service make it back to the Salesforce account record.
Gamify desired behaviours.

The best people to put in charge of keeping the data clean are those who care about the quality of the data. Incentivise those same people with game-like challenges and data quality will only improve.

For example, award points based on a contact records completeness. If a record has 10 fields and only four are required, award points for completing the remaining six. Publishing a league table of average scores grouped by owner, allows users to measure their own data against that of their colleagues. Rewarding the star performers and champions of good record capture with prizes and recognition creates further engagement and awareness for data quality.

Maintaining data quality is paramount to drive ongoing efficiency in your organisation. I hope these strategies will help you reap the benefits of clean data.

In the next installment of this data blog series, I will dive into data migration tactics. Check back in two weeks, or subscribe to this series and receive blogs right in your inbox.

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Is your Salesforce data keeping you up at night?

Bel Gu — Wed, 11 Mar 2015 22:18:17 +0000

We asked more than 1,000 Salesforce customers, “What keeps you up at night?”

Unsurprisingly, the overwhelming response was — data . This encouraged me to start a blog series on data and explore ways to maximise its value. First things first: let’s start by making sure that your data is clean and accurate.

Step 1: Analyse your existing data.

Determine what data you are going to look at, where it came from, and whether it has any special characteristics. You may already be familiar with the data, but consolidating everything we are going to work on in one place will help us document the details of this project as a resource for future data efforts.

What data are we going to look at?

Answering this question requires knowing the desired outcome of the project. For example, Sales wants its Salesforce integrated click to call dialer to work on every lead no matter the source, so we’ll focus on the phone field in Salesforce and any data sources that populate that field.
Where did the data come from?

We need to determine all the different ways our telephone field can be populated. Can salespeople edit the field? Do we collect phone numbers from web forms or during live events? Knowing this will help us control how this field is modified in the future, and help prevent new bad data being added.
Does this data have any special characteristics?

Our click to call dialer works with several phone number formats, but getting our current data into those formats will take work. Knowing both the accepted and current formats of the data will help us determine how much work we have ahead of us, and if we need to use any specific tools.

Step 2: Create a cleanse plan.

Once we are clear about the state of our data and what we are trying to achieve, it is time to create the project plan. Look for ways to break it down into different stages.

What data needs to be cleansed, removed, or enriched?

Because we’ve identified all of the data we are going to work with and where it came from, we can now decide how to address its deficiencies.
Which business processes are causing data quality issues?

Each must be fixed before bringing clean data into production — otherwise, all that data cleaning and enriching will have to be repeated. It could be as simple as placing a validation rule on the phone field that salespeople can edit.
What tools do we need to execute the cleanse?

There are many different kinds of tools: data loaders, cleaners, phone and address validators. Most importantly, as most of the tools cost money, include the costs in the project budget.

Step 3: Define a Governance Strategy.

Before starting the cleanse, a strategy for keeping the data clean must be in place.

Have all the business processes been redesigned?

...And are there checks in place to keep future changes to those processes from adding bad data? Most importantly, as changes are made to processes, determine if we able to see their potential impact on data.
Has data validation been added at system entry points?

Validation should be built in at the user interface, bulk upload, and integration points. Data that enters clean is likely to stay that way.
Does the data have an owner, and are they incentivized to keep it clean?

If an individual understands how bad data makes their job harder, they are more likely to be a champion of data quality. Find a data steward in every department, and make them part of your governance strategy.

Now that your data is clean, what are you going to do with it? How will you keep it clean, and use it to gain more value from your CRM investment? Tune in for subsequent installments in this blog series as we go deeper into data.

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