Anyone working in marketing and sales knows data is growing exponentially. There are an abundant variety of technologies anyone can buy that promises to generate more and better leads, improve conversion, predict outcomes, and attribute program ROI. Every marketing and sales process can now be now data-driven.
The challenge for the modern marketer is no longer a lack of data. But, drowning in data you can’t use is worse than having no data because it costs money to acquire and store all that data. There’s a growing trend of too much unusable data and too many applications that are continuing to get worse, all the while there’s a shortage of available talent for those with experience in analyzing data and extracting insights.
In order to create data-driven marketing and sales organizations, leaders must decide on the foundational data capabilities their organizations must acquire and nurture. The traditional answer is to develop a “360 view of the customer.” Throughout the years, this concept of pulling all your data into one repository has taken on different labels: data warehouse, data mart, master data management, and the latest trending label is Customer Data Platform. All these essentially refer to the same solution with minor variations, namely:
- Aggregate multiple data sources into a single data repository
- Clean and merge data into a single unified schema
- Deploy various types of reporting, analytics, and machine learning technologies to make sense of this data
- Perform various types of discovery, management, and governance capabilities to support this data
This approach works reasonably well for an established company that operates in a mature market and grows slowly. But, it works poorly for any company that operates in a dynamic market and works with rapidly evolving mobile and cloud technologies. These projects typically go through a death-spiral like this:
- 9 to 12 months of analysis and meetings just to agree on what data needs to be collected and what this “uber data schema” should look like. The number of stakeholders and priorities involved makes this a cat-herding process.
- A governance process and committee is put in place to approve any changes to the consensus requirements and schema. Because it’s so hard to get consensus in the first place, the governance committee becomes the “NO! Committee.” In order to have any hope of finishing the project in 12-18 months, scope creep is aggressively thwarted.
- Strict change controls allow the project to move ahead, but the business keeps on evolving at a faster pace and its needs are no longer being met by the project. As a result, the business does what the business always does, which is to take the matter into its own hands and do whatever is necessary to address its changing needs, which means going around the project and doing its own thing.
- When the project is finally completed, it’s 12-18 months out-of-sync with where the business is. The business has already deployed a patchwork of technologies, vendors, and manual processes to meet its needs and keep the lights on.
- If the project is lucky enough to go live, it usually takes so long to make changes that it’s unable to keep pace with the business. It becomes more of a burden than an enabler for the business.
- The “good data” is only available in the central data repository and not reflected in the systems of record, so the automation in those operating platforms such as sales automation and marketing automation still don’t work and require localized effort to improve data quality, which goes out-of-sync with the central data repository. As a result, incorporating information from support, product, and billing systems into sales and marketing automation is out of the question.
Although the project label keeps on getting sexier and technology keeps on getting more advanced, the fundamental approach hasn’t changed. It’s all based on a “build-it-and-they-will-come” approach. Put all the data in one place and people will figure out what to do with it. This utopian approach is why projects like these are so fragile and have horrible ROI. The approach is structurally flawed and doomed to fail from the start.
Instead of “build-it-and-they-will-come”, it is much better to always ask:
- Who is coming?
- Why do they come?
In other words, understand whom the stakeholders are and what they’re trying to achieve with this data. One truism about data that people must understand is that no one works on data for the sake of working on data. The only reason people work on data is to drive analysis, decision, and process. Depending on the specific needs of the analysis, decision, and process, the data must be manipulated to support those specific needs. To think that you can come up with a single data repository and schema that can meet all the different needs of the various stakeholders and projects is naive at best.
So, what’s a better approach than “build-it-and-they-will-come?” The answer is agile orchestration. An organization with agile orchestration capabilities can:
- Rapidly prototype and deliver data-driven automation to meet the unique requirements of each specific analysis, decision, and process, in terms of days and weeks, not months and years.
- Update and evolve the automation as business changes, again in terms of days and weeks, not months and years.
To have such organization capability, the team must have the technical capabilities to:
- Improve and maintain the data quality in each system of record, whether it’s Salesforce automation or marketing automation, to avoid the need for repeatedly cleaning the data for every new analysis, decision, and process.
- Pull only the necessary data from only the necessary systems of record to support each project requirement. Don’t do anything more than absolutely necessary. It’s more important to have the ability to react to changes vs. trying to have a crystal ball that can predict future requirements.
- Manipulate data to support business requirements and integration requirements.
This capability should be made available from a single technology platform, instead of relying on a patchwork of tools. This single automation platform should have the following characteristics:
- Usable by sales ops, marketing ops, and business analysts. Ideally no-code, but at least low-code. Programming and IT skills should not be required.
- Out-of-the-box templates to perform the common tasks sales and marketing use cases commonly required, such as cleaning email, standardizing address, segmenting job title, matching lead to account.
- An automation engine that has sufficient configurability to support 99% of the data orchestration needs.
- Built-in reference data sets to simplify data cleansing and standardization tasks, such as lists of countries, states, postal codes, free and disposable email service provider domains, and job title keywords.
- Out-of-the-box connectors for popular applications, including sales and marketing automation applications such as Salesforce, Microsoft Dynamics, Marketo, Oracle Eloqua, and Pardot.
- Built-in integration to data enrichment services spanning a wide selection of data choices, such as B2B, B2C, firmographic, contact, email validation, social media, mapping, and translation services.
- Built-in security and privacy capabilities to ensure any automation can meet compliance requirements, such as GDPR and CASL.
- Support for application-to-application level integration using REST API.
- Ability to display data in the context of any application or website page so that end users can interact with the data.
Cloud and mobile technologies have fundamentally changed marketing and sales. In order to compete and scale in the digital world, the leading marketing and sales organizations need the agility to keep pace with the changes in buyer behavior and advances in technology. The organizations that will pull ahead of their competition are the ones that can out-execute their peers, experiment, and pivot to what works. Agility must be a core competency of any data-driven sales and marketing organization and, in this day and age, if you have any aspiration of scaling and being competitive, you must have a data-driven organization.
So before you launch a new data warehouse, master data management, or customer data platform project, ask the question, “Will this project give your organization the agility you need for data for the next decade, or are you building a glass house that may not have the practicality to support your rapidly changing business?”
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