Driving forward innovation, new products, and services, helps companies to remain on the top. Innovation brings new customers, opens new markets, and creates exciting opportunities, and this is as true for small businesses as multinational brands. And to innovate in this era, companies are looking to unlock insights and create new products and services with the data they already own or can collect. Business Intelligence (BI) and Data Science (DS) is the key to unlocking the potentially enormous value contained within data. Depending on what you want to do, Data Engineers and Data Scientists will be crucial for a certain set of tasks in the process. Data Engineers collect data, analyze it and transform this Data into “pipelines” for the Data Science team. One can take up Data Science and Data Engineer training to enable their skills.
BI – this short term implies an interconnected complex of modern business management methods, built on modern information technologies and allowing to ensure maximum business efficiency. Data Science is different from BI. DS performs predictive and prescriptive analysis to look into the future. They answer questions “what will happen, when, and why” and what are the next steps to be taken to profit from the future situation or minimize unwanted consequences. For this, DS uses AI and ML.
BI and DS can work together to find insights. What data does business intelligence need? And how can it use it?
Where will data come from
An IDC Data Age 2025 report estimates that the amount of data in the world will be 175 zettabytes (1.75e+14 gigabytes), by 2025. An explosion of connected devices and sensors will make a major contribution to this, but the data all need storing, processing, and making sense of. What bytes of connected devices will contribute to this great amount of data?
- smart homes
- smart cities
- medical devices
- Industrial IoT
IDC also expects that the number of devices connected to the Internet will be 150 billion in 2025. Smartphones, computers, and tablets, already a shrinking percentage of the total, will be an even smaller percentage by the end of the next decade. Humans will no longer be the primary drivers and generators of data; our devices will take over.
IoT devices will generate 90 zettabytes of data (out of the 175 zettabyte total). And of that mind-boggling total, 49% will be stored in public cloud environments, with Enterprise organizations responsible for around 60% of big data storage, processing, and use.
At the same time, IDC predicts that six billion consumers (75% of the planet’s population at that point) will interact with data, in some way, every day, in 2025. Understanding data and data literacy is going to quickly become a must-have skill in the jobs market, and for students of every age in school.
Of course, when it comes to predictions about big data, BI and related subjects, there are many many of them floating around. What do these predictions show us?
What does this all mean for businesses, in a practical sense?
- Human and our devices will generate more data, a lot more;
- Businesses will need to store more data than ever, and process it (securely, to comply with regulations);
- Internet and data providers, and physical networks, will need to cope with a massive increase in data transfer and cloud-based storage;
- Consumers will interact with data more frequently, in more ways than we do now;
- Data literacy will become as normal and essential as basic computer skills (and the education system will adapt to introduce this from a younger age);
- Data itself will need to be frictionless to move and use (following universal data principles, so that open source, unstructured and public data can interact with proprietary data, to unlock new value and insights);
- To make data easier to move and use, global IDs and new standards of universally acceptable and applicable tags are going to need to be adopted.
Big data challenges
For businesses to unlock the true value of data, the cleanliness of that data is one of the biggest challenges that need to be overcome.
According to IBM, $3.1 trillion is the annual cost (of cleaning, and opportunities lost) of unclean data. This means, data that is unstructured, formatted poorly, with multiple versions of every entry, and missing details which make it harder to transfer that data elsewhere and make use of it.
$3.1 trillion is a low estimate of what unclean data is costing the world. Data scientists and BI analysts often need to spend a considerable amount of time processing and cleaning data, often creating algorithms to help with that, before any insights and a new value can be unlocked.
In a New York Times article, data scientists explained that “far too much-handcrafted work — what data scientists call “data wrangling,” “data munging” and “data janitor work” — is still required.” Monica Rogati, Vice President for data science at Jawbone, said that this data janitor work “is a huge — and surprisingly so — part of the job. At times, it feels like everything we do.”
Ways to overcome the challenges
- Start with setting out the goals you want to achieve. For every big data or BI project, there should be a clear goal. It might help to work with a data scientist at this stage because the next thing you need to know is what data you have, and what you might need to achieve this goal.
- Mind what challenges need to be overcome. How clean is your data? More work and even the development of an algorithm or customized software might be required to get it to the stage where questions can start to be answered. Problems solved, and ultimately, new value unlocked.
- Store data in clouds. In older, larger companies, this is often more of a challenge than senior leaders anticipate. Data started to be accumulated, in multiple databases, pools, and silos, long before anyone thought anything could be done with it. Data didn’t use to be a commodity. It was simply a byproduct of whatever a company did. Now we understand the potential that can be released.
- Build interaction of analytics, data scientists and c-suite executives. However, to create new products and services, actionable insights and future-proofed relevance, companies need to put in the work. BI and data science teams need C-suite support and budgets to match. Goals need to be drawn up, and realistic timescales to achieve them. Only then will big data projects run more smoothly and achieve the goals that business leaders have for them.
- Find a trustful vendor. It’s a good point to outsource such kinds of projects in case you lack expertise and experience or it’s your first big data project launch. With time, it becomes convenient to support it with your own sources, but practice shows that it causes much less headache when you entrust the launch to a professional team.
Olga Veretskaya is a Market Observer at Anadea. Olga specializes in writing about available and innovative technologies for business. Frequent areas of expertise include eCommerce, AI, retail, and digital transformation.