5 Business Impacts of Advanced Analytics and Visualization

Avatar photo Team Zaloni March 22nd, 2017

Big data has been around for years and few would dispute the benefits it brings to organizations that have more data stored than ever before, ready to be put to use for that competitive edge. But efforts to break down data silos, derive meaningful insights from that data, and convert those insights into action are easier said than done.

Most organizations now understand that if they capture all the data that streams into their businesses, they can deploy big data analytics to get significant leverage in understanding their customers, forecasting business trends, reducing operational costs, and realizing more profits. Regardless of industry, data analysis and visualization become accessible and impact business in critical ways.

When you consider the influence of big data at a macro level, let’s consider 5 ways that big data impacts your business.

Business Processes for Lean Management

Lean management is all about improving quality and reducing costs. Big data analytics helps to improve quality in industries where inconsistencies are hard to reduce (e.g. Pharmaceuticals, Chemicals, mining etc). Just-in-time concepts in inventory management and waste reduction can go a long way in upholding the principles of continuous improvement in businesses processes.

The application of large datasets, faster computing power, advanced analytics and sophisticated big data modeling all help to progress plenty of lean-management priorities. According to McKinsey & Co, big data could be worth tens of billions of dollars for ‘Lean’ manufacturers in the automotive, chemical, FMCG and pharmaceutical industries, among others. So it’s no wonder why manufacturers are deploying big data analytics to their standard operating procedures.

Improving Target Segmentation

Target segmentation includes customizing the user experience with relevant offers that are tailored to the right audience. Big data analytics could help with radical customer insight by highlighting both lagging and leading customer trends and with predictive models for the marketing department to follow and act upon.

One industry that’s leveraging big data analytics is the Telecommunication industry. Telecom companies have deployed big data analytics to glean valuable insights on customer usage behavior, their purchasing power, and recharge patterns to launch new offers and modify old ones. Their marketing teams use data to create subscription plans to meet consumer demand as well as beat the competition in both price and service quality.

Data Catalog & Governance

Nowadays, organizations have to deal with varied data types and increasing data volumes with regard to data creation, retrieval, storage and analysis. Keeping track of big data is crucial because it helps businesses and organizations with valuable insights via self-service analytics for better decision-making. The metadata acquired from data curation could be put to use through various big data analytics tools for better forecasting, too.

Architecting Data Lakes Book
Smarter Employee Retention Processes Through Predictive Analysis
The days of gut decisions and interviewer bias are numbered – soon to be replaced by evidence-based decision making. The new realm of big data analytics affects every area of the recruitment process including vacancy marketing, talent development, filtering of prospective candidates etc. To put the uses of big data into context and help you to better comprehend how you might use it, here’s a great case summary ‘Copy Xerox’s data analysis model for hiring success’. Xerox Corp had estimated the cost of training each of their call center staff at $5,000, yet many were leaving before Xerox could even recoup their training costs.

The business had traditionally assumed that those with call center experience were more likely to succeed. However, analysis of the data proved that candidates with experience cost more to hire, yet didn’t perform better or last longer than those without experience. The data also showed that those candidates who were active social media users had higher retention rates than other candidates. Another surprising insight was that creative types tended to stay with the company longer than inquisitive types. Analyzing big data helped Xerox to cut the attrition rate at their call centers by over 20% – a significant and tangible financial saving.

Increase Revenue & Reduce Cost

Big data technologies such as Hadoop and cloud-based analytics bring significant cost advantages when it comes to storing large amounts of data – plus they can identify more efficient ways of doing business. According to a recent survey conducted by BARC, over 40% of companies worldwide analyze big data and many are now enjoying a plethora of benefits. Moreover, those organizations able to quantify their gains from analyzing big data reported an average 8% increase in revenues and a 10% reduction in costs.

Big data analytics could also be deployed to significantly bring down healthcare ops costs. A KPMG survey of healthcare facilities found that only 10 percent of healthcare professionals use advanced data analytics tools with both analytic and predictive capabilities. By using analytics to reduce costs in ways that do not impact the quality of care, you can use the savings to improve the service provided to each patient.

By embracing advanced business analytics technology and data presentation techniques, there are greater prospects of companies getting better in decision-making and goal setting. There is hope for businesses to boost revenue and provide better services to their clients. Most importantly, businesses are now able to foster great innovations, taking customer experiences to greater levels. That is the power of big data analytics. For businesses that are yet to embrace sophisticated analytics technology, the time is now.

about the author

This team of authors from Team Zaloni provide their expertise, best practices, tips and tricks and use cases across varied topics incuding: data governance, data catalog, dataops, observability, and so much more.