The excitement of achieving business insights with a Big Data Analytics (BDA) solution that will reduce churn and increase Average Revenue per User (ARPU) often leads to unrealistic expectations before the project has even begun. The truth is that for most big data analytics projects the time from conception and sale to actual production that delivers business insights may be as much as 18-24 months. Taking a pragmatic and practiced approach to big data analytics projects should reduce inflated expectations but will dramatically accelerate the delivery time of the working solution to drive business value in as much as half or more.
Value acceleration begins with an assessment of business drivers resulting in recommended use cases that deliver value. What are the business problems to be solved? What are the desired results? Project owners have to ask and answer these important questions. A good rule of thumb here is to assess where the greatest value can be achieved quickly and then implementation of greatest value areas first. Following a familiar cliché, “don’t try to boil the ocean”, but rather find one (or two) problem areas to focus on, drive these incremental project(s) to completion, realize the value, and in the process gain experience and expertise with the BDA solution so that those learnings can be implemented on the next project, and so on, in an accelerated mode. It is important to determine how to measure business value of use cases in order to help achieve this prioritization.
Consistent and proven methodologies for implementing BDA projects are needed to deliver more timely and consistent results. It is the process of identifying the data and information needed to address the business challenges and questions that enables the business insights and monetization.
Data > Information > Insights > Monetization
Users need to understand the full value capability of the BDA solution and the required data inputs to support it. Can it address the business problems? Do you have the requisite data feeds? Do the data feeds contain what you need and are they timely? What reports do you need? Do you have the human resources for the project?
To facilitate the solution adoption, ensure rapid adoption across organizations, and realize measurable value, a five part approach is recommended that includes Training, Quickstart, Best Practices, Resident Expert and Automation.
To state the obvious users must be trained on the use of the selected BDA solution. But the training approach can be can be modified to accelerate the project. Taking a boot camp approach that gets users on keyboards using the product in the shortest amount of time (2-4 hour training sessions) will help expedite the learning process. Continued training modules are added to further system learning and expertise.
Getting a BDA project up and running is best accomplished by targeting a limited set of objectives and deliverables. Project owners must determine a few Key Performance Indicators (KPIs) and/or Key Quality Indicators (KQIs) to use as measurements for business value and identify a few reports to address the specific project(s).
There are many best practices for implementing Big Data Analytics projects but two important ones are data quality optimization and process innovation.
Data is the foundation for any BDA project. Without smart data you cannot have great analytics. Data quality optimization is paramount to delivering great analytics. That means following a process to assess, analyze, and optimize data quality, accuracy, availability and integrity.
Process Re-engineering starts with looking at the People, Processes and Systems and understanding their interdependency and optimizing.
For People one must define who are the users or beneficiaries of the system, how will they use the system and data and for what purpose? Companies can have strong people with strong processes but without an equally strong system operations will not scale.
For Process one must define how data gets managed and disseminated; how system and changes are maintained to enable continuous business decision making. Companies with strong system and strong processes but that lack strong people will results in underutilized assets and unrealized investment benefits.
For System one must define what data the system has, how accurate is it and what business decisions system it is capable of enabling through its functionality. Companies with strong systems and strong people but without strong processes lead to continuous manual intervention, slow response and inconsistency.
To accelerate BDA projects its best to consider utilizing Professional Services resource(s) with expert knowledge of the BDA solution as well as carrier network and analytics experience. The resident expert not only helps accelerate the first project delivery but in doing so can train the project in team in best practices that can be employed furthering the learning and experienced gained and helping to accelerate the application to the next project.
Automation should start with well documented processes as that makes a successful process repeatable and extensible. Continuity audits are taken to ensure compliance with documented processes. Assessing process readiness for business critical areas and then implement findings of audits and improve disaster readiness.
Following these approaches and best practices will aid in setting more realistic expectations for Big Data Analytics projects and help to accelerate the realization of value with business insights and monetization. Having access to rich, real-time, smart data that offers carrier scalability is essential to delivering valuable analytics. With increased competition and accelerating business it is has never been more urgent to realize business insights.
To learn more about how smart data enables great analytics go to NETSCOUT.