An enterprise-wide data analytics system pulling data from multiple sources, correlating and presenting results in relevant, insightful visualizations to enable prompt, informed decisions is the dream of many organizations. Those in the lead already reap the benefits of faster, high-accuracy, proactive decisions that data analytics provides.
Getting to that point requires exquisite planning and execution by stakeholders from top to bottom and across departments in order to achieve an implementation that is useful, maintainable and flexible enough to handle ever-improving data analytics technology and an increasingly competitive business environment.
Don’t Go Too Big Too Fast
Data analytics systems are most valuable when shared across the organization. Therefore, cross-departmental input and commitments are vital as well as a high degree of project transparency. Collecting requirements and then creating an all-at-once solution months or quarters later is courting disaster. That sometimes works for limited software development projects, but data analytics initiatives necessarily demand a much larger scope in both time and space.
Adopt an incremental mindset from the start by applying a less risky phased and flexible tack to your data initiative’s development. The project should produce regular and continuous adjustments to functionality, features and metrics without unnecessary thrash. This paradigm is most likely to produce a quality product with high acceptance across the business.
Gain Executive Stakeholder Buy-In
With the correct attitude regarding initiative progression, gain C-Suite buy-in via detailed assessment and quantification of business needs to which data analytics capabilities can add measurable value. These come from conversations with department heads, people managers, project managers and line workers involved in operational activities that probably benefit from insights provided through readily accessible business analytics.
Collect Technical Requirements
After executive endorsement, pinpoint technical needs, features and functions required to create a system meeting the project’s strategic goals. These requirements are derived from various operational aspects:
- Existing data analytics-related processes, e.g. CRM, and their supporting software and infrastructure
- Identifying existing data sources and creating a baseline of the what, when and how of data storage and processing
- Where applicable, data sharing patterns, especially where data transformation steps are required
- The current collection of in-house, open source or cloud-based tools and services utilized
Turn Technical Requirements into KPIs
Concurrently with technical requirements acquisition, work closely with stakeholders to develop meaningful metrics and KPIs. Examples include metrics around analytics data acquisition, storage and cleaning. Marketing metrics might measure campaign response. High-interest sales metrics centre on conversions and ROI, whereas support metrics include customer satisfaction and retention. Be open to innovative metrics that a new data analytics system could enable.
Ask for Resource Commitments
While collaborating on KPIs, initiate frank discussions with regard to workers and material that stakeholder departments or teams are willing to provide to the project. The benefits of such commitments should already have been incorporated into specific KPIs that benefit them.
Choosing a Software Model
Inconsistent use of software models, such as open source, in-house or cloud-based SaaS is common in companies. This often results from an organic acquisition of tools and projects over time. Your data analytics implementation will not correct old problems, but its choice of software model should be based on technology availability, costs and the ability to scale and adapt as your company expands.
For instance, even with a strong in-house IT development capability, the benefits of basing your data analytics implementation on cloud-based SaaS are compelling.
First of all, removing the constraint of higher capital needs and their approval alone makes a forcible argument for choosing pay-as-you-go cloud SaaS. Furthermore, this complements your phased approach as infrastructure and services are easily scaled and maintenance is automatic. Finally, today’s cloud SaaS from the best providers is fully customizable, which enables rapid functionality development and ease of modification during ongoing development.
- Expect dirty data, especially from social media, and deal with it at the source where possible. Employ tools such as import.io, diffbot and ScraperWiki in this battle. Especially during testing, consider importing customized, on-demand data sets.
- Be sure data analytics dashboards and reports are highly customizable but easy to learn. This area is the face of your initiative for the majority of users. Also, ensure dashboard functionality works for your mobile users.
- Build in extensibility. This means anticipating new data sources and leaving room for the latest in predictive analysis technology.
- If you are using a phased, results-oriented approach, you will have plenty of opportunities to celebrate small victories. Relish and share these milestones.
Data analytics have a proven track record of providing enterprises with streamlined and on-target decision-making improvements based on “right-time” data flows from inside and outside the company. Implementing the best system for your company requires intense and thorough planning followed by step-wise development and deployment.
Realize that even as your project begins achieving its end goals that ongoing business needs and changing markets call for continued growth of your data analytics capability. If you already chose cloud-based SaaS as your software core, then the next growth and adjustment phase will be much easier than the first, especially if you stick to your iterative development paradigm.
If you have questions about how to get started working in the cloud, let us know. We’re happy to share our knowledge and set you on the right path.