Analytics projects that utilize big data or analytics are increasingly prevalent but currently a heightened hazard of failure, according to Gartner, Inc. Analytics leaders can improve the likelihood of success by best practices.
“Although big data and advanced analytics projects risk many of the same pitfalls as traditional projects, in most cases, these risks are accentuated due to the volume and variety of data, or the sophistication of advanced analytics capabilities,” said Alexander Linden, research director at Gartner.
Failure to properly comprehend and alleviate the risks can have a number of unintended and highly impactful consequences. Those can include loss of reputation, limitations in business processes, losing out to competitors, inefficient or wasted use of resources, and even legal sanctions. Gartner also predicts that “by 2018, 50 percent of business ethics violations will occur through the improper use of big data analytics.”
Why has Business Intelligence failed?
Traditionally, big data has focused on descriptive analytics to articulate the story of what happened. The reports generated from this tactic are excellent at identifying scapegoats, pitfalls, and passing blame. They are less good at identifying growth opportunities and improving the bottom line.
Following key best practices will help analytics leaders to advance the likelihood of success, and they include:
Identifying clear business necessity and value.
Almost everything needs to be a business rather than a technology solution. Before companies start collecting big data, they should have a clear idea of what they want to do with it with from a business sense.
Value co-creation of value with customers.
An overall business objective should always be to customers. If one of the initiatives is about big marketing result, then it should be about how to set up customer-centric marketing, how to provide targeted dynamic advertisement, how to engage customers and how to manage personalized experience.
Identify what part of the business would benefit from quick wins.
Look for opportunities that will show quick successes within no more than three months. Success brings more people to the table.
Balancing Analytic Insight with the Capability of the Organization to Make Use of the Analysis
Because analytics can only be valuable in organizations that are willing to embrace alteration, it makes sense to bond investment in analytics to a level that matches the organization’s ability to use the resulting insights. Analytics may not be the most suitable approach:
- If relevant data is absent
- When here are high stages of ambiguity
- Where there are entrenched opposing points of view
- In highly advanced or novel scenarios
In these cases, scenario planning, options-based approaches, and critical thinking should also be merged into analytical tactics to better support the organization’s ability to take action.
Create a road map that progressively builds the skills of your society.
It’s significant to create a road map that allows you to progressively build the required assistances within your staff, minimalize risk and capitalize on preceding successes to gain more support. In the organization, there will be new roles and responsibilities such as the data scientist, who possesses a blend of skills that includes statistics, applied mathematics, and computer science.
This is different than any current choice support solution.
With big data, organizations should look for new capabilities, such as: using advanced analytics to uncover patterns previously hidden; imagining and exploration to help the business find a complete answers, with new types and greater capacities of data to best characterize the data to the user and highpoint important patterns to the human eye; enable operational decision-making with on-demand stream data by making floor employees into analytic consumers; and turn insight into action to drive a decision – either with a manual step or an automated course. And most important be ready for speedily increasing benefits and complexities from the six Vs.
When you are in a process of starting a big data journey, consider this question: What should our big data with deep analytics roadmap look like to achieve our objectives?