Data processing is in itself a simple and straightforward process, but big data can be as intimidating as its name promises.
From the second you start reading this sentence until the moment you reach the end of it, thanks, largely in part, to mobile solutions, cloud computing, and IoT, data has been produced in huge quantities.
The data generated come in all shapes and forms:
- Internal and external
- Structured and unstructured
What’s even more astounding, there’s no stopping big data from growing.
Big data adoption
According to a whitepaper published in 2017 by research group IDC, worldwide data will swell to 163ZB by 2025, with the majority of this data being created and managed by enterprises. In turn, organizations will be looking to invest heavily on big data campaigns by buying or developing proprietary software, upgrading their servers, and increasing staff headcount with people of high analytic skills, such as data scientists, research analysts, data architects, and business analysts
As more and more organizations embrace big data to power their day-to-day operations and integral decision-making functions, understanding the obstacles inherent in big data is the first step to overcoming them.
1. Data collection
Data is everywhere. For organizations new to big data, one significant challenge is identifying the best possible ways to collect and organize the data relevant to their activities.
Below are some data collection methodologies:
- You can extract data from established systems in your enterprise such as transactional systems: HR, accounting, payroll, time tracking, project management, and other systems.
- For marketing, sales, and customer relationship teams, data can be collected from cloud-based web forms such as Google Forms and survey platforms like SurveyMonkey or SurveyGizmo. Data collection that doesn’t involve actively surveying customers, there are various tools you can use. These include Google Analytics, Qualaroo, Clicky, Kissmetrics, and Crazy Egg. All this data can then be stored in a wiki or knowledge base to ensure that all teams are on the same page, reducing cross-team friction.
- For logistics, access control, and even laundry and library management, there’s RFID (radio frequency identification).
- For real-time location tracking, which is useful for freight management, aviation and security, recreational sports such as hiking and cross-country skiing, GPS (global positioning system) offer many benefits.
- To control and manage inventory, bar codes and QR codes help warehouse managers efficiently track the movement of their merchandise.
- Location-based data from smartphones and other mobile devices provide marketers and retailers with a glimpse into their customers’ buying habits and the shops they frequent.
- There’s also beacon technology that retailers, hotels, airlines, and B2B marketers can use for ambient context identification.
2. Data usage
The explosion of data is as scary as it is exciting. From an instructional point of view, too much data can cause analysis paralysis, as educators become unsure of which data types to focus on helping their students succeed.
In the case of businesses, as more and more communication channels open up for customers to interact with brands, without the right systems in place, messy interaction tracking can result in poor customer service and your company losing even your most loyal customers.
A recent Wikibon report forecasts big data revenue growth to over $103 billion by 2027. While these figures include big data hardware and software, it will be software that drives the majority of growth with systems that analyze datasets for usable insights, such as in the following use cases:
- Internet of Things. The Internet of Things (IoT) is a web of interconnected devices that communicate with each other via the cloud. IoT-enabled devices are equipped with data-gathering sensors, and the data collected is processed by cloud applications in real time to generate the insights you need. For example, IoT allows you to turn on or off your thermostat at home using your mobile phone, wherever you are in the world.
- A holistic view of the customer. Merchants want information into how customers use their websites, which landing pages convert well, what calls-to-action resonate with visitors, at which point shopping carts are abandoned, or purchases are made, which products sell well, how customers perceive the brand on social media, among other things. Also, big data allows you to accurately pinpoint at which stage of the buying cycle a lead or prospect is on, enabling you to send targeted messaging and offers.
- Weather. The sensors in mobile devices designed to map atmospheric readings gather weather data that is then used to live-update weather maps. This is one of the things that IBM showcased at CES 2019.
3. Data security
Privacy is a big deal for everyone. People’s trust in your integrity lies in how well you can keep classified information classified and what security measures you implement to protect the data they entrust to your business.
A ComputerWeekly.com article asserts that:
Very few organizations are likely to build a big data environment in-house, so cloud and big data will be inextricably linked.
A TDWI article by Accenture’s Raghuveeran Sowmyanarayanan introduces the different platforms to secure big data:
- Hadoop
- NoSQL databases
- Cloud-based services
But data storage in the cloud would always beg the question: How secure is my data?
The same ComputerWeekly.com article cited above mentions a technique known as attribute-based encryption, which is a new cryptosystem for fine-grained sharing of encrypted data, to rectify the drawbacks of already existing encryption methodologies.
Final word
Big data can only get bigger, and as the needs of businesses evolve to better address ever-changing customer requirements, data processing responses are expected to happen in near-real time, if not real time.
To reap the benefits offered by big data, take note of the obstacles outlined above and the ways to overcome them.