Big Data Analytics Automating Data Driven Decisions

Big Data Analytics: Automating Data Driven Decisions

Big data is a term for large or complex data sets. They are large in a way that traditional data processing systems and applications are inadequate to deal with the complexity. Roles of Big Data include analysis, querying, data curation, visualization, sharing, storage, and information privacy. Big Data analytics represent the next big wave of new innovations and technologies, and has the potential to transform how organizations manage talent, operate, and create value.

Big Data: What is purpose driven data?

Purpose-driven data refers to big data sets that will enhance performance and support specific functions. This means that different types of big data should be isolated and analyzed depending on the specific use of the set. There are times when big data points will be hard to fine, of course, not all big data points are of equal value. But it’s these big data sets that will help meet specific objective that provide the most value. In order to identify the purpose of data points:

Big Data ask the right questions

Big Data: How can we reduce costs? or How can we improve the productivity of each team member?

1. Ask the correct questions.

The questions the organization should ask depends on informed priorities. Examples include “How can we reduce costs?” or “How can we improve the productivity of each team member?” Think about how the organization can align essential functions with important use cases. In a world of hard budget limitations and time constraints, analytics are more valuable for good questions rather than vaguer ones such as “what patterns does do the data sets show?”

Big Data: Combine sources of information to make insights sharper and more accurate

Big Data: Combine sources of information to make insights sharper and more accurate

2. Consolidate insights.

Big Data insights generally live within boundaries, and combining sources of information can make those insights sharper and more accurate. Companies too often focus on a single big data set in isolation without considering what other sets convey in the junction.

Big Data: Useful data points come in all different shapes and sizes.

Big Data: Useful data points come in all different shapes and sizes.

3. Embrace “fuzzy” data.

Useful data points come in all different shapes and sizes. Additionally, they can often be dormant within the organization, in the form of text reports or slideshow presentations. Quantitative teams too frequently disregard these data sets because the quality may be too poor, dated, or inconsistent. To optimize available data, organizations should strive to build a strong model where data is measured by input scores and reliability, and not simply dismissed because the data is too fuzzy or that it doesn’t feel like “data.”

Big Data: Ensure data sets are clear and simple

Big Data: Ensure data sets are clear and simple

4. Ensure clear, usable outputs.

While the most sophisticated algorithms can work wonders, they can’t present or speak for themselves in boardroom meetings. Many times, data scientists fall short in effectively articulating what needs to be communicated. This isn’t surprising, as technical roles don’t prioritize presentation skills as much as quantitative expertise. Ensure data sets are clear and simple, and not overly complicated when using it to garner support.

Big Data: combine small advances systematically across bigger processes, the payoff can very much be exponential

Big Data: combine small advances systematically across bigger processes, the payoff can very much be exponential

5. Think small and execute big.

The smallest edges can make the biggest difference. The impact of big data analytics is more than often manifested by incrementally small improvements. If a company can stretch a single process into its smallest parts and integrate improvements where possible, the payoff can be substantially large. And if the company can combine small advances systematically across bigger processes, the payoff can very much be exponential.

Big Data: Everyone should be aware of the process, strategy, and milestones

Big Data: Everyone should be aware of the process, strategy, and milestones

6. Provide effective change management.

Company culture makes adoption possible. From the moment the organization starts the analytics initiative, everyone should be aware of the process, strategy, and milestones.

The best indicator for a successful analytics program is the commitment of executive leadership. It takes a C-suite mindset and perspective to help foster collaboration across business functions, align initiatives, and insist that changes be made and insights be used. Big data analytics is valuable, and correctly leveraging data sets ensures that the decision-making process is accurate, effective, and purposeful.

How can WGA help with your Company’s Big Data Analytics?

WGA is passionate and dedicated to helping clients make and execute the big decisions on: strategy, operations, transformation, technology, organization and compliance. WGA’s guiding belief is that as trusted advisors; we must measure our results from the enduring financial success of our clients. This belief and passion can be seen in our growth, our people, our services, and our relationships.

Our experience across various industries allows WGA to offer singular and unique objective recommendations and execution services that will give our clients the ability to adapt, renew, and prepare themselves to succeed in a turbulent environment. We provide Strategy Consulting that can help with strategic, scenario, and contingency planning in regards to big data analytics capabilities, and Management Consulting services that can facilitate the integration of any necessary structural changes resulting from Big Data improvements. From strategy to operations, we are committed to helping our clients build their functional skills and sustain performance. We recognize that only through our clients’ success can we achieve ours.

Posted in 2017 Trends, Business Analytics, Enterprise Systems, Information Technology, IT Consulting and tagged .