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Five Common Use Cases of Big Data Adoption by Organizations

Big Data Analytics Platforms continue to be adopted by different organizations to get unique insights for their business. In this post, I will cover five common use cases that we are seeing with our customers who are adopting Big Data for their Business Analytics need.

1 ) Data Warehouse Modernization

As organizations look to modernize their business analytics platform, whether it be moving away from spreadsheet processes to a fully automated data warehouse solution, or simply to modernize their legacy data warehouse and reporting platform, the requirements remain the same – a technology, which is cost-effective, open source, highly scalable, and relatively fast to deploy. Big data solutions are a leading contender to deploy a modern enterprise data warehouse.

Building a traditional data warehouse starts with documenting business requirements, designing a data model to support those requirements, and using a data cleansing and consolidation tool to organize data.  A significant amount of time is spent gathering requirements, data modelling, ETL and reporting.  Organizations are demanding technology to simplify the process of data storage and data management. Business users want to be able to answer their own questions as needs change without relying on IT.

A big data solution can not only fulfill your business analytics requirements, but also modernize your platform, ensuring you are future proofing your architecture to address the growing volume, velocity and variety of data that your organizations may face as the need for data insights continues to grow.

2 ) Customer 360°

Imagine the possibility of getting all your customer data from your CRM applications to marketing and sales to commerce, service and social. The ability to tie together these disparate data sources enables cross-functional analysis between different departments. A unified customer experience means the service department has historical sales data at its fingertips and knows long-term customers from one-time buyers. It means the customer service representatives know what your customer is calling about. And you know the value of every customer as does your retention team.

The platform that can address this need should be scalable to allow for exponential growth given customers create data with laptops, mobile applications and a myriad of different devices. The platform should be designed to stored petabytes of data allowing for scalability to zettabytes of data. Traditional data warehousing can’t do that.

A modern approach is the path to get insights out of the data you have without spending significant time focused on data management, data structure or data cleansing. There’s rapid adoption of big data platforms by organizations looking at transformational technologies to fulfill these types of requirements. Moreover, the ability to perform predictive analytics on the data can take provide insight into investments like never before. Should you open a new store in southern California? Would offering a new color of widget impact sales?

With big data architecture, you can capture data on consumer behavior, perform advanced analytics, and predict the return on investment (ROI) even before product launch. Promote your product at a price and place where the solution predicts the greatest lift or ROI. This allows organizations to formulate a logical marketing strategy, quantify benefits and increase its ROI. With big data platforms, you can achieve a 360-degree view of the customer in a cost effective and highly scalable manner in a shorter amount of time.

3 ) Internet of Things

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The Internet of Things (IoT), broadly defined as a network of internet-connected objects that collect and exchange data using embedded sensors, is unquestionably a leading source of an incredible volume of data.

What can we do with all the data generated by these sensors? Turns out, a lot. There is significant value in being able to collect the data via everyday objects like machines, server logs, weather sensors, manufacturing plants, to name a few and analyze them. Manufacturing, energy, construction, agriculture and transportation are some of the industries that are realizing tangible benefits by leveraging IoT for data-driven decisions.

According to IDC in 2018, “IoT spending among manufacturers will be largely focused on solutions that support manufacturing operations and production asset management.” Manufacturing plants use sensors to monitor performance of machines and systems ensuring maintenance issues are handled proactively without having to shut down operations, thereby continuing to maintain the throughput and customer expectation. There’s significant cost savings in fixing faulty parts in expensive equipment if detected early on before a minor problem becomes a major concern.

Tracking product movement by monitoring the weather is another area that could use IoT to get valuable insights. If a company’s sales are impacted by inclement weather, like a nor’easter, a manufacturer of small generators may want to align its supply chain to be able to manage the expected spike in sales for the day or a given period.

Predictive algorithms using historical data, tying that with current sensor data to be able to make more accurate predictions that will impact profitability is what separates forwarding thinking companies from those entrenched in dated current systems and processes.  Big data platforms are widely used today to fulfill these types of business requirements.

IDC forecasts worldwide spending on the Internet of Things to reach $772 billion in 2018 with manufacturing, transportation and utilities, making up 45% of the projected spend.

4 ) Recommendation Intelligence

When you search for an item on Amazon, the web commerce platform learns about your interests from your shopping and purchase behavior. It starts making recommendations for similar products as well as complementary products you may want to buy based on what other customers bought together with a given product. You may not have been shopping for the additional item from the onset, but the mere suggestion that others bought batteries when purchasing the same electronic toy may be enough for you to add it to your cart.

Netflix is another great example of recommendation intelligence in action. When you select a movie or TV show you want to stream on Netflix, it learns from your likes and dislikes via thumbs up and down and uses predictive models to make recommendations around your interests. There are very successful companies capitalizing on this algorithm. Pandora is yet another example. Based a single song or selection of artists, the platform intelligently starts playing songs to our liking. The more feedback given regarding preferences, the more intelligent it gets and plays songs likely to suit the mood or ambience desired.

This engine can also be used to handle customer calls and make appropriate recommendations that may result in upsell or cross-sell opportunities. Today’s customers interact with these platforms expecting the intelligence to be built-in. It starts to become second nature. The key technology behind this intelligence is a big data analytics platform. A cost effective way to store huge volumes of data, to be able to perform advanced analytics on the collected data sets and build predictive models. Use these models to make appropriate recommendations. Most importantly continue to improve the model based on user feedback.

What are use cases relevant to your industry? Deployment of a solution capable of recommendation intelligence can directly result in increased revenue and hence the bottom-line growth. It’s a competitive differentiator.

5) Sentiment Analysis

United Airlines lost almost $1 billion dollars in market cap for the removal of a passenger by force. A video of the encounter was widely shared not only by cable, but also by social media. The airline was boycotted by frequent flyers and new passengers globally. The backlash from sharing of the video taken by other passengers was swift and painful for the world’s third largest airline.

In the age of a social media, it only takes one improper handling of a situation and a disgruntled customer to share his story that could start trending and before you know it, everyone globally is talking about it. In the case of United, it was the bystanders that took to social media and not the passenger ejected.

Snapchat lost $1.3 billion dollars because of a single tweet shared by Kylie Jenner. It wasn’t something bad about the platform, it was a simple question posed about platform usage. Social media can be an extremely powerful channel to build a brand and maintain a reputation. If there are problem areas resulting in angry customers sharing their frustration and concern, there has to be a strategy to be able to address those concerns and problems early on before it catches on and starts trending.

For visibility into these insights, organizations need tools that can allow customer representatives or marketing to monitor and analyze various social media platforms such as Facebook, Twitter, Instagram, and Snapchat in real-time. The traditional approach to analyzing these types of data is not going to work, it has to be real-time and the technology should handle very large and scalable volumes of data. Organizations are using big data platforms to be able to perform sentiment analysis and to structure a customer representative team to address such concerns as they arise.

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Shiv Bharti

Shiv is the Practice Director of Perficient’s National Oracle Business Intelligence Practice. Shiv has solid experience Building and Deploying Oracle Business Intelligence Products. He has successfully led implementation of over 75+ Oracle Business Intelligence and Custom Data Warehouse Projects. Shiv has worked in multiple industries and with clients that include fortune 500 companies . He has Expertise leading large global teams, as well as in-depth knowledge across multiple verticals and technologies. Prior to 2008, Shiv was a member of the Oracle and Siebel Core Engineering Teams and responsible for the Design and Development of numerous Business Intelligence Applications.

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