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Getting the Most Out of Your Salesforce Data
By Glyn Heatley, Vice President, Cervello
The crux of this hypothesis however, are the following assumptions:
a) you have good data within Salesforce to start with, and are, of course, executing a strategy to sustain it,
b) you have extended the point in time operational reporting that is native to Salesforce to one that is more analytical in nature, and
c) you are able to enhance Salesforce data with other external complementary data sets.
Often the focus of data quality for Salesforce is primarily on the “customer.” Is it complete? Do you have inaccuracies? Are there inconsistencies and duplications? Organizations must extend this focus however to other data sets whose structure, content, and quality must also be managed if there is a demand to report and analyze on this CRM activity. This is also important if there is a need to integrate Salesforce data with other enterprise applications in order to extend insight. For example, integrating actual sales from the general ledger with sales forecasts provides the business with variance analysis and performance trending.
Master data, which the “customer” is part of, typically describes the entities, places and things that take part in business transactions. Examples include vendors, products, accounts, and sales territories. Reference data typically consists of codes and categorizations that are used to provide meaning to other data. Examples include currency codes, status codes, product classifications and customer segmentations. Applicable to both is the concept of managing relationships within these data sets as well as alternative views of these relationships. These hierarchies allow you to further arrange or categorize the data according to relative importance or inclusiveness. Sales territory for example, where city rolls up to state and region to country, allows you to get insight at both a granular and aggregate level. Without these data sets and hierarchies, you are unable to get a true 360-degree view of your customer and all of the activity that relates to them. Additionally, you are unable to understand easily the performance of your CRM activities from an aggregate or segmented view.
Within Salesforce it is traditionally difficult to manage this data and ensure its quality. Functionality was built to support CRM activity and not the governance and maintenance of master and reference data. In Salesforce, it is a manual, intensive process whereby, it is difficult to put rules around the creation and validation of data, including security as to who can create, update, and access the data. This is because hierarchies are very static and simply cannot be changed easily. With hierarchies, limited to accounts there is poor visibility to other “related” objects such as contacts and opportunities. For example, you can’t rollup related records and there is an inability to view, navigate and report on hierarchies. This ultimately hinders the efficiency and effectiveness of sales teams when it comes to upsell and cross sell opportunity identification and execution.
Master data, which the 'customer' is part of, typically describes the entities, places and things that take part in business transactions
Maintaining and managing such data for certain organizations hits a tipping point when there are large and numerous data sets and a demand for complex and alternative hierarchies for reporting, future planning (e.g. reorgs), or a need to integrate Salesforce data into multiple other enterprise applications. At this point, organizations should consider centralizing their management of this data by leveraging a specialist master data management solution. Ideally, this would be a solution with extended functionality that supports data governance processes such as workflow and data change notification and approvals. Such an available best-in-class solution is Oracle’s Enterprise Data Relationship Manager (DRM) and Data Relationship Governance (DRG) toolset. These tools allow for the centralized management and synchronization of master and reference data across transactional and analytical systems.
In parallel to working to ensure the continued quality and completeness of Salesforce data, organizations should consider how to leverage further its data for more strategic decision making. Inherently, Salesforce provides operational type reporting that is more near term in nature. For example, what are all current opportunities or what activity has been performed this week. However, more strategic value can be attained by extending the usage of Salesforce data by integrating it into an analytical platform and enhancing with data outside of Salesforce. Here, larger historical data sets along with other application data sets should be integrated to answer such questions as:
• Which sales territories are taking longest to close opportunities?
• Which products and services have had decreasing sales?
• What was the net impact of a rate increase to our price book?
• How is performance to plan and what does first quarter revenue forecast look like and so forth?
By providing such an analytical environment, analysis can be provided at various levels of master data hierarchies, extending the audience of reporting from sales and marketing to senior executives down to individual contributors.
When selecting a best in class analytics platform, organizations are faced with a deluge of options to consider, many touting rich feature functionality that always tends to impress. In determining the right solution for your organization, the following capabilities should have significant influence on your selection:
• On premise or cloud analytics? On premise, analytics solutions are hosted on your on-site server or on a partner’s host facility therefore requiring infrastructure investments (i.e. hardware, software, resources, and support). Cloud analytics are solutions and services delivered through hosted services such as cloud models. Cloud analytics can offer enhanced agility, scalability, and reduced total cost of ownership (TCO) as the vendor is responsible for the infrastructure as well as necessary maintenance and upgrades over time.
• Data management? Does the provider have a solution with an end to end architecture that allows for the sourcing and integration of data from disparate data sources? Does the solution manage the data model and storage of data? Does it provide information access via various rich methods, including pixel-perfect reporting, ad hoc query, data discovery, and dashboards? Or does the platform require that some form of data platform and content, for example, an enterprise warehouse, already exist? And if so, has data already been sourced from enterprise applications and pre-prepared for a presentation tool to provision reporting and analysis functionality to end users?
• Data Integration? How well can the analytical platform connect/ interface with sources of data? Does the platform provide any transformational and automation capabilities that can be used to manipulate source data into the required format for reporting and analysis?
• Security model? How advanced are the security capabilities of the platform from user authentication leveraging existing corporate standards to granular, role-based, data level, functional and content security?
• Mobile capabilities? Is the goal to put data into everyone’s hands from anywhere? Is information available for access via mobile devices? Can report and dashboard designs be optimized for mobile device viewings?
The importance of these considerations and resulting investment were traditionally determined by the strategic vision for the analytical platform, whether the focus was to extend and enhance the reporting capabilities on Salesforce or to establish an enterprise reporting and analytical platform that includes Salesforce. Either way, the continued advances in rich feature functionality offered by SaaS analytics providers and the low cost of entry for organizations, makes analytical platforms viable choices and worthy of serious consideration.