Core data strategy principles apply however big your data is
While data strategies are far from a recent development, many businesses struggle to get the right levels of engagement in the strategy and business functions questioning the accuracy of the data underpinning day to day decisions.
Big Data only makes the challenge of developing an effective strategy even more difficult. While organisations previously had more limited amounts of data on consumers, there are now huge volumes of data available – these require different skill sets to be employed and embedded in business processes. Even those who may have had significant volumes of data previously must now consider how to efficiently integrate potentially valuable new data sources into their business.
One thing is for sure, there is now a general consensus that data is a most valuable asset that, when farmed in the right way, will deliver organisations increased opportunities and insights to provide a stronger competitive advantage.
Bearing this in mind how can organisations develop a strong data strategy that resonates through the business to achieve cross functional buy in? A pragmatic approach is required to avoid data paralysis, one that focuses on business benefits to prioritise critical activities versus nice to haves.
These 6 key steps will help businesses to define their big data strategies
Step 1: Determine what support is required to meet established business objectives
This is a fundamental step that many organisations appear to skip, delving right into the detail rather than taking a high level view of what they are looking to achieve with their data. These high level vision statements vary across organisations and sectors although recurring themes stand, such as ‘knowing our consumer’ or ‘developing increased customer advocacy’.
Once this has been agreed, focus on the success metrics. Each organisation will be likely to have at least three or four supporting initiatives; these are likely to include obvious objectives such as increasing subscriber sign up, active customer base or increase product purchase. Again these basic steps ensure that the business has a clear view of what it is trying to achieve before embarking on such a project, thus obtaining key stakeholder buy in.
Further down the line, referring back to these can provide clarity as to whether a task is in or out of scope of the project.
Step 2: Define how the business will need to use the data to deliver on its vision
Having agreed on the basics, review what the business needs to do to achieve the established goals. This may include: reviewing current data collection mechanisms and marketing opt-ins to ensure maximum customer contactability; using advanced analytics to enhance personalisation and content strategies; ensuring customer touch points are aligned to deliver consistent messaging; improving the ability to react to behaviours in real time as the consumer is engaging with the business.
Step 3: Identify the necessary data to meet your goals
Having developed the vision, it becomes much easier to review exactly what data will be necessary to underpin the strategies. As you are defining the information, it is useful to assess whether you have highlighted items that address every part of the customer journey – the data an organisation requires to achieve an effective sales conversion from prospect to buyer is very different than managing an ongoing relationship with a loyal consumer. It is important to focus on what data you want – whether it is available at that time or not – keeping in mind that other organisations can also provide valuable data resources.
Linking back to the overall vision should allow the business to assess which information is priority – data items that cannot impact any of the overall objectives should be disregarded at this stage. This is important as now more than ever there is an abundance of data.
Step 4: Assess the state of current data sets and initiatives
Once decided on which elements of data are fundamental, organisations need to review their current data assets in line with this.
This should cover the following areas:
• Data coverage: where are the gaps in data items coverage – can these be plugged by enhanced data collection or utilising external data
• Data quality: Check how clean the current data is, assessing volumes of duplicate records across individuals and whether data items captured across different source systems been
captured to consistent documented rules
• Data Linkage: different sources of data may operate on different references, e.g. email subscriber lists compared to website transactors. Organisations need to assess how best to
link this data together to meet their objectives, again this could be enhanced by considering external data assets
• Data capture: current processes and usage across different business units
• Marketing options: assessment of current processes to determine if they are leading to optimal contacts and whether there is a case to try and enhance current opt ins across
• Data redundancy and governance: if data is collected at multiple points, review which data should be taken from what source as data is integrated
• Meta data capture: if there is intelligence embedded into data items, such as source codes or campaign extracts, is the underlying data captured in an easy to access way?
• Documentation: reviewing the current data assets will likely uncover a lack of data dictionary documentation. As part of the process rigorous documentation should be adopted to
ensure that time is not wasted
Step 5: Align the current state of data with targets and formulate a plan
Define and prioritise what processes, tools, resources and skills need to be adopted within the business to get the data into the required state and then to analyse, deploy and measure ongoing impacts. You will probably need to adopt a staged approach based on an informed view of what will drive most business benefit.
Step 6: Maintain responsibility and regularly review
Data should be seen as an asset so any data strategy initiative needs high level sponsorship and ongoing commitment to ensure that decisions are underpinned by accurate data. It is not a stand-alone project but should develop over time as the business objectives change or new data becomes available – thorough ground work at the start of the project will allow an easy way to evaluate new requirements.
Second opinions can make all the difference
An external perspective can be vital in ensuring that any data strategies are planned with the broader business needs in mind – otherwise planning processes can unintentionally be hampered and narrowly focused.
This second opinion can provide suitable guidance and support on a number of areas, including:
• Assessing single customer view, marketing data mart build and execution technology to support the relevant access and insight capabilities
• Helping to define the initial goals and the necessary programme elements to achieve these
• Providing proven analytics tools to support any business case
• Data cleansing and optimisation
Tackling Big Data is a must for any forward-thinking business. Using these 6 key steps will see your Big Data projects have greater direction and alignment with business objectives.