The Future Of Business Intelligence

In today’s modern world, business intelligence is of utmost importance as data in business practices take center stage. BI will stand in the prime of all this and will direct future marketing strategies and business decisions. After all it is proven that companies using analytics are 5x more likely to make faster business decisions.

Today we look at BI’s prospects and why it will be center to all major business decision making in 2017 and beyond:

1. Big Demand for BI analyst

As the graph of Big Data increases, the no of people require to crunch that data and arrive at an intelligent solution for a business enterprise will simultaneously increase. A report by Mckinsey global institute states that in the next four years the US alone would face a shortage of 1.5 million managers and 1,90,000 analysts.

2. Make it universal

Currently, BI is specifically targeted to internal employees and information workers. The more it’s spread out and the more relevant it becomes to everyone, it will be a Game Changer. Outside the purview of corporate boundaries, it should reach out to suppliers and customers. Business intelligence has the power to enable businesses to compete more effectively and change people’s way of working.

3. Easier analysis of past data

As the cost of storage for data shrinks rapidly, it would be much easier to analyze past events and data. For example, currently due to data cost we are able to analyze just an economic cycle but in future, we can analyze much more than that. This will help us in forming a strong pattern. This will help businesses to bear just minimum losses in the event of a recession. History is a great teacher and BI will help us to avoid repeating the same mistake.

4. Personal analytics will be norm

As more and more data gets collected in the system, the future definitively holds good for personal analytics. It can primarily be used for self-improvement. Also, it will be used for improving family life and local communities. BI won’t be geeky in the future and everyone will be able to self-analyze. There are tremendous opportunities for software vendors to develop tools regarding personal analytics.

5. Business Intelligence will be a collaborative effort

The future of the BI would bring different tools and platforms together. Today individual BI is the norm, tomorrow it will be collaborative. Shared and immersive analytical experiences would increase in great proportion. The form factor of the devices will also gradually shift from small to large. This will help a group of data scientists explore data in real time basis.

6. Data knowledge Gap will shrink further

Data knowledge Gap in the next few years will further narrow from a group of data scientists to a normal manager or executive. As more forms of data visualization grow, more people will become familiar and comfortable with it. Major companies will also mandate training in BI.

With the above-mentioned points, there is ample evidence that Business Intelligence is here to stay. We can safely assume that, not only will it make a big impact but also it will scale up rapidly.

Why invest in Machine Learning

From automated solutions to consumer feedback systems, what is the one thing that integrated business technologies use to improve operations? What has emerged as a new age approach to understand clients better, enhance sales and purchase cycle, interpret clients’ pain points, or create consumer-centric products and services?

Yes, it is the use of machine learning and Big Data analytics.

Using machine learning to boost the efficacy of Big Data analytics is what will drive modern businesses into the next decade of growth and sustainability.

How it all began?

The sales and purchase cycle involves customers at every level. While making purchase decisions, consumers take quite a few aspects into account. Let’s take a look at these crucial factors:

• Recognizing needs: That’s the first thing every consumer needs to do. Recognizing the purchase needs is critical to finding the right products. Therefore, every customer needs to identify his buying needs and objectives.
• Figuring-out solutions: Once buyers are aware of their needs, they can make the right decisions. Proper identification of the problem will lead to effective solutions.
• Decision-making process: After figuring out the solutions, consumers will have the opportunity to take effective and successful decisions.

Machine learning and Big Data analytics play a vital role in this context.

Here’s what you need to know about the benefits of machine learning in understanding customer behaviors.

1. Machine learning decodes consumer behavior

Targeted and proper understanding of customers depends on studying their behaviors. Crucial insights into their behavior and actions will help you identify their preferences and choices. Machine learning and big data help you gain insights into consumer behavior in real-time. If you wish to accelerate your bottom line, it will be imperative to leverage the benefits of machine learning and big data analytics.

2. Matching products with consumer preferences

Accurate algorithms are an integral part of the machine-learning technology. These algorithms play a crucial role when it comes to determining the price points. Retailers will have the chance to determine price points and product availability, thus matching the right set of products with specific consumer choices.

Some of the leading brands are making the most of this technology and helping consumers make better purchase decisions. Machine learning reduces possibilities of getting confused while shopping, thus ensuring an unparalleled and satisfying experience.

3. Consumers can fulfill their demands

Machine learning coupled with big data analytics can present a crystal clear picture of consumer behavior. You know your consumers’ purchase behaviors in the past, which helps you anticipate their buying preferences.

With the machine learning technology, you can gather real-time customer information. From identifying the links clicked by them to finding out contents they choose for social media sharing, machine learning helps you comprehend customer behavior in details.

Parting thoughts

In an age where consumers are the most crucial parts of the sales cycle, personalized shopping experiences will be the key to promoting your brand. By helping you understand consumer behavior, the machine learning technology will take your brand to unsurpassed success.

Part 2 of  Confluence Of Big Data and Mobile – The Next Revolution by Vishwas Mudagal, CEO of GoodWorkLabs

Internet of Things

Mobile Big Data doesn’t always have to come from people, it also comes from background services and devices themselves that leave a trail of information capturing our every action. We call it Internet of Things (IoT). IoT has the ability to effectively ease the current big data projects. The IoT is a concept which explains how Internet will expand itself when the physical objects, like healthcare sensors, wristwatch displays, home entertainment systems, smart posters and many more, will be connected to the Internet.

(image courtesy)

As experts describe the IoT, actuators and sensors embedded in the physical objects – from racing cars to pacemakers, are linked through resourceful networks (both wired & wireless), often using the similar  IP (Internet Protocol), which power the Internet. When objects will start sensing the environment and communication, they will become valuable tools for understanding the complexity and responding to it hastily.

It’s predicted that The Internet of Things has the power to make the current big data projects look tiny and miniscule. Paul Bachteal, the senior director of the Americas technology practice for business intelligence vendor SAS, said that “billion is the new million” when IoT moves from a concept to reality.

Applications of Mobile Big Data in diverse industries  

Let’s take a look at some of the hot trends in a few industry sectors.

Retail

Big data has started to play a significant role in delivering insights into shopping behaviour of consumers and many smart retailers are introducing changes in their stores in order to deliver what the customers expect from them. Mobile Big Data has started giving retailers a factual and precise understanding of the buying patterns of different shoppers, how they move around in their stores, what they look out for and what attracts them. Social media inputs through location enabled services provide valuable data as well. Utilising this information, retailers can benefit more from their existing and potential customers while improving their bottom line.

To be concluded in Part 3.

We are doing a three part series of the chapter ‘Confluence of Big Data and Mobile–The Next Revolution’ by Vishwas Mudagal, CEO of GoodWorkLabs, that was published in the book: Understanding Big Data.

We are sure this would be a useful read for mobile and big data enthusiasts and for people who want to know what the fuss about big data is all about?

Part 1

The next decade is going to be the decade of data explosion. Even today, data and information have become the most significant aspects in every area of the global economy. Companies are continuously churning out burgeoning volumes of data pertaining to their customers, operations, processes and suppliers. All this data which is being getting collected worldwide is broadly termed as “Big Data”. What is driving Big Data? Will the confluence of mobile and big data trigger the next revolution? I would say Yes. Everything at this point in time directs us to take this conclusion. So, let’s talk a bit about the drivers and the impact of big data on mobile for different industries.

To begin, let us first define Big Data. Big Data refers to an unending accumulation of all types of data, whether structured or unstructured, whose confinement, storage, management and analysis is beyond the ability of any typical relational database. According to some experts, big data is data set that meets three attributes – Volume, Variety and Velocity. And of course the fourth V, Value. No one would bother to store and analyse data that cannot be valuable.

Big Data on Mobile OR Mobile Big Data

The mobile devices, especially since the launch of iPhone and now the Android platform, have become major contributors in the collection of Big Data, as these are used by people to practically do everything. Mobiles are tailor made devices for Big Data Innovation. Be it accessing social networking platforms, shopping, maps, read books, measure fitness, watch television shows or using it for traditional purposes of communication using voice, text and chat. To highlight this point, let’s consider the largest social network on earth—Facebook.  48% of Facebook users log in daily through mobile and 49% of Facebook’s revenues are coming from mobile ads. This mobile usage reveals significant information about the users and their behaviours in diverse scenarios. Imagine billions of people on mobile every single day, producing such humongous data that if we analyse it in a right way it would take humanity to the next level, like nothing else ever has. Because never before there was an opportunity presented to humanity to measure what we collectively do or like/dislike in our daily lives.

Drivers

So what are the drivers? Of course smartphone sales growth is a top reason and the smart phones themselves are becoming superior, and they pack a ton of computing power to do practically everything that a laptop could do and much more. According to the IDC reports, the amount of data created will grow by a factor of 44 from 2009 to 2020 and mobile and ‘Internet of Things’ will be at the forefront of this data generation. According to another IDC report that analyses the worldwide smartphone market for 2013–2017, it is believed that smartphone vendors will ship a total of 918.6 million smartphones in 2013. From there, shipment volumes will grow at a CAGR of 16.0% before reaching a total of 1.5 billion units shipped in 2017.

To emphasise this point, here’s a quote from Eric Schmidt, Google executive chairman, in 2010 about data explosion.

“Between the birth of the world and 2003, there were five exabytes of information created. We now create five exabytes every two days.”

If analysed effectively, this exponentially growing data and its collection through smartphones can provide significant insights on users, their behaviour, their sentiments and even their physical movement patterns. Companies can use Mobile Big Data analytics to develop better understanding about the users to optimise the services delivery and engagement tactics.

Mobile devices (which include smart phones, tablets and traditional mobile phones) are linked with Big Data in two different yet significant ways—“Sources”  for accumulation of Big Data and “Delivery Mechanisms” for Big Data.

How efficiently can we utilise real-time big data analytics and put it to practical use by making it action-oriented data? It can happen most effective on mobile. Analysing the data collected by mobile devices is important, however, Big Data practitioners must also leverage the mobile devices’ near-ubiquity in delivering relevant products and services to its users. Mobile devices can also serve as delivery mechanisms for Big Data analytics to cutting edge workers who need access to timely information in order to carry out different tasks.

BYOD (Bring Your Own Device) devices will also play a significant role as the data collection and reporting mechanisms for Big Data. This is what I term as the cross over between the Enterprise and the Consumer space, providing an impetus to Big Data that covers the intersection of these two sectors.

Continued in Part 2.