Big data – and the way organizations manage and derive insight from it – is changing the way the world uses business information. To stay relevant, its integration needs to work with many different types and sources. While operating at different latencies – from real-time to streaming.
Wondering how to build a world-class analytics organization? Make sure the information is reliable. Empower data-driven decisions across lines of business. Drive the strategy. And know how to wring every last bit of value out of it.
Cloud, containers, and on-demand compute power – a SAS survey of more than 1,000 organizations explore technology adoption. Illustrating how embracing specific approaches positions you to successfully evolve your analytics ecosystems. See more details on the New Analytics Ecosystem.
But, is the term “data lake” just marketing hype? Or a new name for a data warehouse? On that note, Phil Simon sets the record straight about what a data lake is, how it works, and when you might need one in this article.
What is Big Data?
More often, big data is a term that describes the large volume of data – both structured and unstructured – that inundates a business on a day-to-day basis. But, it’s not the amount of it that’s important. Rather, it’s what organizations do with that which matters. Bearing in mind, it can be analyzed for insights that lead to better decisions and strategic business moves.
In other words, the term refers to data that is so large, fast, or complex that it’s difficult or impossible to process using traditional methods. Not forgetting, the act of accessing and storing large amounts of information for analytics has been around a long time.
The large heap of data generated every day is giving rise to the massive information and correct analysis of it is obtaining the need for each organization.
Basically, the concept gained momentum in the early 2000s. When industry analyst Doug Laney articulated the now-mainstream definition where we can summarize it with the seven V’s.
The 7 Vs that defines it include:
- Volume: Organizations collect data from a variety of sources. Including business transactions, smart (IoT) devices, industrial equipment, videos, social media, and more. In the past, storing it would have been a problem – but cheaper storage on platforms like data lakes and Apache Hadoop have eased the burden.
- Velocity: With the growth in the Internet of Everything (IoE), data streams into businesses at an unprecedented speed and must be handled in a timely manner. RFID tags, sensors, and smart meters are driving the need to deal with these torrents in near-real-time.
- Variety: It comes in all types of formats – from structured, numeric data in traditional databases to unstructured text documents. As well as, emails, videos, audios, stock tickers, and financial transactions.
- Variability: The meaning of words in an unstructured format can change based on context.
- Veracity: With many different data types and sources, quality issues invariably pop up in big data sets. Veracity deals with exploring a data set for data quality and systematically cleansing that it can be useful for analysis.
- Visualization: Once it has been analyzed, it needs to be presented in visualization for end-users to understand and act upon.
- Value: It must be combined with rigorous processing and analysis to be useful.
Hadoop is a savior for large Data Analytics and assists the organizations to manage it effectively according to this article.
Commonly used terms nowadays
Inevitably, much of the confusion around it comes from the variety of new (for many) terms that have sprung up around it. Below is a quick run-down of the most popular ones:
- Algorithm — mathematical formula run by software to analyze it
- Cloud (computing) — running software on remote servers rather than locally
- Data Scientist — These are experts in extracting insights and analysis from it
- Hadoop — a collection of programs that allow for the storage, retrieval, and analysis
- Predictive Analytics — using analytics to predict trends or future events
- Internet of Things (IoT) — refers to objects (like sensors) that collect, analyze and transmit their own (often without human input)
- Web scraping — the process of automating the collection and structuring of sitemaps and sets from web sites (usually through writing code)
- Structured v Unstructured data — structured set is anything that can be organized in a table. So that it relates to other sets in the same table. An unstructured set is everything that can’t.
- Amazon Web Services (AWS) — a collection of cloud computing services that help businesses carry out large-scale computing operations without needing the storage or processing power in-house
Why is Big Data Important?
According to Research and Market reports, in 2017 the global market in this field was worth $32 billion. And by 2026 it is expected to reach by $156 billion.
The main importance of it doesn’t revolve around how much data you have, but what you do with it. Moreover, you can take it from any source and analyze it to find answers that enable;
- cost reductions,
- time reductions,
- new product development and optimized offerings, and
- smart decision making.
Particularly, it helps you determine the root causes of failure in businesses. As well as the ability to analyze sales trends based on analyzing customer buying history.
On the other hand, it helps determine fraudulent behavior and reduce risks that might affect the organization. And when you combine it with high-powered analytics, you can also accomplish business-related tasks.
Such tasks include:
- Determining root causes of failures, issues, and defects in near-real-time.
- Generating coupons at the point of sale based on the customer’s buying habits.
- Recalculating entire risk portfolios in minutes.
- Detecting fraudulent behavior before it affects your organization.
While understanding its value, it continues to remain a challenge.
Whereby, other practical challenges, including funding and return on investment and skills, continue to remain at the forefront. Especially for several different industries that are adopting its use. So, more importantly, however, where do you stand when it comes to this field?
You will very likely find that you are either:
- Trying to decide whether there is true value in Big Data or not.
- Evaluating the size of the market opportunity.
- Developing new services and products that will utilize Big Data.
- Already using Big Data solutions. Repositioning existing services and products to utilize Big Data, or
- Already utilizing Big Data solutions.
With this in mind, having a bird’s eye view of Big Data and its application in different industries will help you better appreciate it.
As well as what your role is or what it is likely to be in the future. May it be in your industry or across various industries.
Who is Focusing on it?
Big data is a big deal for industries. The onslaught of IoT and other connected devices has created a massive uptick in the number of information organizations collect, manage, and analyze. And along with it comes the potential to unlock big insights – for every industry, large to small.
Industry influencers, academicians, and other prominent stakeholders certainly agree that Big Data has become a big game-changer. In most, if not all, types of modern industries over the last few years. As Big Data continues to permeate our day-to-day lives, there has been a significant shift of focus from the hype surrounding it to finding real value in its use.
Top 10 Big Data Applications Across Industries
Additionally, deep learning craves it because big data is necessary to isolate hidden patterns. And also, to find answers without over-fitting the data. With deep learning, the more good quality data you have, the better the results. See more details on What is Deep Learning?
As for the SAS Industry, you’ll get solutions that meet your industry’s specific needs – no matter the size of your organization. From the world leader in business analytics software and services, meet the SAS company. Connect with SAS and see what we can do for you.
Generally, most organizations have several goals for adopting Big Data projects. While the primary goal for most organizations is to enhance the customer experience.
Other goals include;
- cost reduction,
- better-targeted marketing, and
- making existing processes more efficient.
In recent times, data breaches have also made enhanced security an important goal that these projects seek to incorporate.
Customer Big Data Analysis Solution
As an example, “BizXaaS BA” is a customer-data analysis service using Big Data techniques. This service combines the analysis infrastructure and standardized analytics report. Users can create various types of reports that give new insights into their customers’ behaviors.
This solution can be used for applications such as sales improvement by customer targeting and customer cancellation prevention. Furthermore, NTT DATA provides business consulting services and optimal services for creating customized reports.
Equally important, “Xrosscloud®” is NTT DATA’s total M2M solution comprising a cloud platform, and wide scope applications covering areas. Such as disaster prevention, healthcare, and transportation. Additionally, NTT DATA has a strong track record of successfully delivering Hadoop projects of different sizes.
From tens of servers up to thousands of servers, learn more about how NTT DATA covers the entire lifecycle of a Hadoop project here.
Takeaway,
Whether or not you believe the hype about whether it will change the world, the fact remains that learning how to use the recent influx of data effectively can help you make better, more informed decisions. The thing to take away from it isn’t it’s largeness, it’s the variety.
You don’t necessarily need to analyze a lot of it to get accurate insights. You just need to make sure you are analyzing the right one. And to really take advantage of this revolution, you need to start thinking about new and varied sources that can give you a more well-rounded picture of your customers, market, and competitors.
With today’s technologies, everything can be used as data — giving you unparalleled access to market factors. I hope you enjoyed reading this blog article. Feel free to Contact Us if you’ll need more additional support or information. You can also donate to support our ongoing projects here.