Big Data is making an enormous impact on business growth and productivity, with companies reporting that methods of data collection and analysis can fundamentally change the way they conduct their business. Here, the key benefits of big data analytics and business intelligence are examined in the framework of the modern, data driven approach to business (infographic).
Many people promote the concept of ‘making the complex simple’ as a means of improving communication and understanding. Ask any politician! Of course, it is not always helpful or possible in areas of science, technology or medicine, for example.
But complicated processes and relationships have to be managed in the business world and a good place to start is to look carefully at core functions and how they will support business outcomes. Or, perhaps better stated, what functions are needed to deliver critical results. That is certainly true in the increasingly sophisticated world of handling, managing and using data for sales and marketing.
The complexities of data obviously vary widely between huge global corporations and small businesses. However, the primary data management objectives remain constant – and straightforward. Whatever the size of your business it’s worth bearing these in mind in order to create, manage and measure a large team, or prioritise the workload of a much smaller one.
They are particularly helpful in creating a ‘RACI’ model – Responsible/Accountable/Consulted/Informed – of management control. This will enable you to focus on outcomes, not process, whether you are managing a big team – or just yourself!
Here are those 5 objectives:
- Data must meet sales and marketing execution needs
- Data must be clean and accurate
- Data must be timely
- Data must be accessible
- Data must be compliant and secure
That’s it. Everything you want to do with data comes under one of these headings. You need email addresses in order to make your data effective? That comes under objective number 1. Your data needs to link to Salesforce or a DSP platform? That is covered by objective number 4.
Let’s look at these in a bit more detail and expand from core principles to tasks. Then we will come to a model of data management. This first table gives an expanded definition of what is covered by each objective, and some of the tasks that may be included. Obviously, these may differ according to the size and type of organisation involved. A healthcare company storing patient information will need much more stringent legal review than a small B2B company, for example.
|Sales and marketing execution||Ensuring that the data gathered and hosted is able to meet all sales, analytics and marketing requirements in terms of matching the addressable market, the information needed to engage with groups and individuals through omni-channel media and measure results||Collation of sales and marketing needs; converting needs into a data strategy; aligning strategy with current and prospective data sources whether in-house or 3rd party; 3rd party data selection; continual review of data in execution mode; creation of 360-degree view; enforcement of data governance processes|
|Clean and accurate||Data must be standardised, complete and up-to-date in order to facilitate matching, modelling and campaign execution.||Implementation of data quality systems and processes; defining and implementing clear supply and quality agreements with internal and 3rd party suppliers; audits; development or purchase of appropriate internal or external tools and reference data|
|Timely||Data must be refreshed as frequently as needed (including real time if needed) from all internal and external sources including customer and prospect activity to meet sales and marketing needs||Alignment with sales and marketing requirements; configuring tools and processes across platforms; monitoring data flows, ensuring the age of data at collection/receipt|
|Accessible||The data must be accessible and available to sales and marketing teams through all required CRM, MAT, DSP, analytics and other platforms and tools||Creation of data flow schemas, data architecture, tool and platform selection, process development across tools and channels, data integration.|
|Compliant and secure||Data must be gathered and held in compliance with all laws and regulations, and made secure through robust structure and processes||Monitoring all laws and regulations; checking data collection, consent and processing are compliant; reviewing and implementing security procedures; stress testing data security.|
So, let’s see how we can convert these tasks into a RACI model. Firstly, a reminder of what RACI is. It is basically a tool to enable management and measure and enforce accountability of interrelated functions driving towards a common business goal. It ensures clarity of roles in big teams, and can act as a checklist for smaller groups or individuals who have to ‘multi-task’.
Here are the definitions:
R = Responsible = The person who performs the work.
A = Accountable = The person ultimately accountable for the work or decision being made.
C = Consulted = Anyone who must be consulted with prior to a decision being made and/or the task being completed.
I = Informed = Anyone who must be informed when a decision is made or work is completed.
The model below is illustrative and includes some typical but not definitive job titles. Nor is it comprehensive. Some of the ‘R’ functions may well be outsourced to contractors or outside agencies and, because the functions are broad, they contain more tasks and therefore roles than many RACI models. In smaller companies, the titles will obviously be compressed a lot. In very large ones there will be even more fingers in the pie. The names in the ‘A’ function should always be limited as much as possible to clarify authority.
RACI data management model:
|Sales and marketing execution||Demand generation teams, field marketing; sales and marketing operations; data strategists; data acquisition lead||CMO||CIO, CTO; CRO; head of sales; head of analytics; MAT and CRM teams||Sales and marketing teams; analysts; sales and marketing operations|
|Clean and Accurate||Marketing managers; data quality teams; MAT and CRM administrators||CIO/CTO/CDO||CMO, head of sales; head of analytics||Sales, marketing and analyst teams|
|Timely||Data architect; systems engineer; MAT and CRM administrators||CMO/CDO||Sales and marketing VPs||Sales, marketing and analysts|
|Accessible||Data architect; systems engineer; MAT and CRM administrators||CIO/CTO/CDO||CMO, CRO, sales and marketing VPs||Sales, marketing and analysts|
|Compliant and secure||Legal and contracts teams; data architects; data acquisition lead||CIO/CTO; General Counsel; DPO||CMO||C-Suite; sales and marketing|
CMO = Chief Marketing Officer; CIO = Chief Information Officer; CTO = Chief Technology Officer; CDO = Chief Data Officer; CRO = Chief Revenue Officer; DPO = Data Protection Officer.
(I am grateful to my former colleague Chris Baylis for his review of the roles and titles.)
While this type of model can embrace all stakeholders and ensure accountability, two questions remain:
- Who is in overall charge of sales and marketing data?
- What is the role of data governance?
The first question is easier. It should normally be the CMO, although the role of a Chief Data Officer is becoming more common. This may lie inside or outside of the marketing group. However, the second question is connected to it and there should be some separation of powers because there is bound to be a degree of conflicting priorities between sales, marketing and therefore revenue imperatives and data compliance – including permission and opt-ins.
Large corporations will probably have a separate data governance role, or at least a data governance function usually within the larger CIO group, that will be responsible for all data. The new GDPR regulation that will take effect in Europe in May 2018 recommends the creation of a Data Protection Officer (DPO) position. But regardless of legal requirements, big companies need to have a means of adjudicating who has access and control of the data, when and for what reason.
Data is usually complex and management is often hard. Let’s try and keep data management as simple as possible!
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