Over the last few years, Big Data has become one of the biggest buzzwords for businesses worldwide. With data of all sorts being generated in record amounts each year, capturing and analyzing this knowledge would give businesses greater visibility into their clients and their markets than ever before, and maybe even encourage them to foresee what may happen in the future.
Here are just one of many amazing big data stats: They submit 204 million emails per minute, upload 2.5 million pieces of content on Twitter, send 277,000 tweets, and publish 216,000 photos on Instagram.
There is a massive amount of data out there, just enough to learn. But it can be time-consuming as well as challenging to make sense of millions (maybe billions) of data points without powerful technology, particularly when this data becomes unstructured. That is often the case for digital online data in the form of news stories, social media messages, feedback from blogs, and much, much more.
Besides, such is the difficulty of this cycle that a reaction towards big data has been somewhat current. Now there are concerns about the value of big data being overstated because it is too “huge” and unruly.
There are two primary forms of Smart Information, which are often addressed by industry experts. Another type is information collected by a sensor, then sent to a neighboring collection point, and acted on before being sent to a database for Analytics. Such data comes from Intelligent Sensors, in particular within the Industrial Things Internet (IIoT) networks. The other kind of Smart Data is the Big Data stored and waiting to be translated into actionable information. Data heading to and from a Smart Sensor is “sensor data” for this report. The word, Smart Data, would apply to Big Data which was tested for useful information.
Consumer Journey Analytics weaves hundreds of communications through multiple channels from the company internet. It incorporates thousands of activities to create a journey for the customers of a company. It is a data-driven methodology that is used to identify, interpret, and impact the experience of consumers. However, if the input is “false,” it is both annoying and offensive. Further, it may result in the loss of a client. The Customer Experience Assessment (or Customer Analytics Voice) utilizes tools and techniques to collect the perceptions, thoughts, and feelings of the customer. Consumer Analytics speech stresses the customers’ mental state.
Machine learning is often a method of preparation with Artificial Intelligence applications but can also be used as a system of understanding and decision making. While Smart Data’s use and prominence has grown, it has also been used with Machine Learning algorithms designed to find Business Intelligence and insights. Machine Learning allows companies to process data lakes and data centers, thus generating smart results.
Traditionally, companies pursuing Big Data Business Intelligence have used Data Scientists who spend time searching for trends and correlations within the databases of an organization.
Decisions are made during the scanning and filtering process of creating Smart Data as to which data should be filtered and which should be released. During this method, Machine Learning and Artificial Intelligence (AI) employ specific criteria. AI is a continuous attempt to create wisdom inside computers, allowing them to function and act like human beings. Artificial Intelligence has provided autonomy and can address specific goals. Financial services companies, for example, can use AI-driven Smart Data for consumer identification, fraud detection, market analysis, and enforcement.
Organizations with less knowledge of Big Data often gather everything and then archive it in a Data Warehouse, Data Lake, or often what a Data Swamp is. We obtain Big Data intending to use it “until we decide to use it.” While these companies may believe they are gathering quantitative data for years, the data may lose quality or quantity or may even be in the wrong format.
Their money would be best used to collect data appropriate for their company. An enterprise can be knowledgeable about the data it collects and retains in a Data Lake. Data takes time and money to collect, compile, and manage. Collecting Intelligent Information can be an effective strategy for small and medium-sized organizations, rather than “pure” Information. The emphasis on Smart Data collection helps a company to use cost-effective solutions to handle it. Collecting only the essential data will minimize the use of Self-Service BI systems, preventing workers from getting lost in the mass of irrelevant data.
Smart data collection is not just about removing the excess data. Smart data can come from various outlets, and an agile enterprise can combine these resources to develop a highly focused business intelligence model.
The point of view is right away. Big data is unusable, lacking order. It is just a collection of random knowledge that would take years to absorb, and may not provide any results even then. But when the form can be easily overlaid and evaluated, big data tends to become smart data. At Talkwalker, we have a way to explain just how this is going to happen, and how it can be a little like searching for a partner in life.
From a machine, all social data is just words on a page through different sources, including Twitter posts, Facebook posts, news articles, websites, and discussion sites. The first step, as you would on Google, is to search for a particular subject in that data.
Let’s claim we’re typing “Talkwalker” into our framework for social data analytics. At this point, we would have a very long list of URLs or post titles in no particular order, without any other criteria or filters.
With such a narrow filter, the knowledge that we can obtain from such details is, as you can guess, also quite restricted. All we’d ever say is how many times a specific word has been listed online. The detail is by no way meaningless.
It may, in turn, be important Information for businesses looking to expand their brand awareness.
We were addressing important industry and technical challenges that often discourage companies from incorporating big data into their everyday decision-making process. We said that those problems are solved through smart data. We’ll dig at five best practices in this article that you can use to start moving from big data to smart data today.
Most of the data is dumb, hard to find, impossible to connect with other data, and hard to comprehend. When data is stupid, so big data is very stupid. Here are five moves to make it smart. But we need more to make our social data truly perform.
Stupid data demands that we know the exact position of a specific piece of information that we are interested in. We may need to know a particular part number that serves as the primary key in a database or Hadoop cluster, or we may need to learn unique internal Identifiers that are used in three different systems to recognize the same person. To deal with this, we use the simple keyword search or canned queries for stupid packaging data — solutions that allow us to extract known data but don’t help us pose new questions or uncover new details.
Dumb data is provincially quite relevant. Within the boundaries of the specific silo in which it was formed, it has identification and significance. Nevertheless, stupid data outside of that silo is useless. When put in the same sense as data from a dozen other business applications, a self-increasing integer key that uniquely identifies a client within a CRM program is highly ambiguous. A short text string such as “name” used to identify a specific data attribute within a key-value store such as MongoDB can clash with different characteristics from other big data stores, databases, or spreadsheets when letting loose in the wild.
Even when we find relevant details, we are restricted in our ability to understand stupid data since it is usually not well-described. Dumb data is represented by database, row, and column names or records and critical identifiers, which are often brief, fuzzy, and undefined outside of a specific data store context. We have been grappling with this for decades by developing applications that have hard-coded awareness of what data is in which column in which database table. Secure coding of this Information into every layer of software from the application layer to the business logic and the user interface renders it incredibly complicated. Complex software is susceptible to glitches and is costly and time-consuming to update, undermining our ability to provide business decision-makers with the most up-to-date and appropriate data promptly.
Because most of the data is hard to find, merge, and interpret with other data, the meaning ends up being reduced. The commitment and expense needed to use dumb data effectively to drive business decisions are so significant that we only use it for a few business problems, particularly those with stagnant and predictable requirements. For example, we can implement a standard BI method to monitor and maximize product revenue by area. Still, we can not extend the same analytical rigor to staffing client initiatives, recognizing the tactics of rivals, delivering constructive customer support, or any of hundreds of other day-to-day business activities that would benefit from a data-driven approach.
When data is dumb, so big data is very stupid. We now have the ability for Hadoop and other big data systems to gather data at will in quantities and varieties not previously seen. It, though, further exacerbates the difficulties of locating, integrating and interpreting at any given time the data we need.
The next step would be to add potentially a variety of filters. For example, an easy one would be a time-frame. Including evidence from the last week or the previous two years alone? Or just the last hour, right?
We also want to learn the media channels from which those web “mentions” come. Are they tweets on Facebook, videos on YouTube, news articles, or blog posts? And how many of each one?
Understanding the nation where posts originate, and the language in which they are published is also essential for global companies. Are there more reports originating from the UK or the US? Is there more Spanish or Portuguese web material about the brand?
Our unruly ocean of social media has been broken into more manageable channels with just the inclusion of a few filters. Instead, these flows can be merged and separated at will to create multiple data streams, enabling you to separate the details you need.
For example, you could build a stream of data for a UK company that has just launched a new product and began a vast Twitter campaign by looking at UK Twitter results over the past two weeks.
You have already made a massive difference in the accessibility of your data at this point. With these various filters, you can now get a better idea of exactly where and when references are coming from about a particular topic.
At this point, there are plenty of observations to collect, but to make the social media smart, you need to go a step further.
When filtering this knowledge allows us to build useful data streams, analytics can help us turn it into concrete lessons we can use to help a business grow.
Using advanced analytics, we can now look at the most important themes that occur in this data, and see, for example, which words are most correlated with some products. We can also see which influencers have the most significant impact on a company on Twitter or Facebook, whether positive or negative. Or we can see who was the most popular (e.g., whether it’s men or women who speak about a topic) or we can examine the emotions about a particular campaign or product.
It is this cycle of finding, extracting, and reviewing large volumes of unkempt data that brings it to life as a source of business actionable knowledge. Learning that your company has been referenced twice a million times in the last month is not insignificant. However, learning that your brand has been discussed twice a million times with a quarter of those mentions originating from “men based in the US use Twitter on the evening of May 1st because of a news article written in the New York Times” is much more insightful.
Each degree of filtering and interpretation offers you crucial insights into your social data, but your data gets smarter each time you go further.
Of course, being able to utilize and integrate these ideas into the business strategy would fall on the people in the company with experience in each field. Yet, smart technology enables experts to jump from point A to point B quickly.
And once you’ve been able to refine the data to this point, it can be used in countless ways.
As more and more individuals are using social networks, websites, and forums to discuss the issues that matter to them, the Information that can be gained from these outlets become more reflective of the opinions of the general public, and therefore more useful and accurate.
In a crisis, a company should track all of their brand’s mentions through multiple media outlets, and then search for the hashtags commonly connected with their brand to identify and respond to potential sources of social media backlash.
In marketing ads, statistics on awareness rates can be paired with sales data in particular countries or languages to get an accurate picture of how the market is reacting to a specific campaign.
For ads, it is essential to analyze the most used keywords around a particular topic and find the right targeting goals.
Uses of advanced social data to this degree are practically infinite and are rising all the time. Including supermarkets and utilities to political parties and Charities, companies, and organizations in all sectors see the importance of social data within their business.
The next critical stage of the process is to deliver the data to the right people at the right moment. Also, it is essential to provide such data in the correct format. Online media analysis can be used across agencies, from the C-Suite to customer service. The fast distribution to each team of the related insights means that you get the most out of the information you have discovered.
A sometimes understated advantage of the big virtual data is its ability to be integrated with other sources of data. Facebook data can be mixed with other customer data, data from that variety of wearable devices, or other “connected” devices (i.e., the internet of things, or IoT) via APIs or by merely transmitting performance. The integration of these strong data sets produces an even more reliable and nuanced view of the online and offline operations of your client, which gives a more detailed picture of your consumers.
There has been a discussion on the benefits of big data for some time now. But this is an unlikely course. The ability to manipulate this knowledge should only improve as technology advances and the familiarity of processing big data sets increases. Through turning big data into smart data, companies can keep finding crucial lessons for their business and making choices based on better, more relevant data.