Often the words such as data, information, and insight are used interchangeably, but these words are not similar, they have different meanings. Understanding those differences can help you tailor your program to benefit your business.

What is data? How does that turn into information and what kind of insights does it yield?

These differences are confusing right! But the distinctions are the simple ones you see how they work together. Let’s see what their definitions actually mean.

What is Data?

Data is raw with unprocessed facts that we capture according to some agreed standards. Data can be in the form of numbers, images, audio, transcriptions, etc. While working on analytics projects, the first task is to go through the client’s data structure or even normalize it.

What is Information?

Information is a collection of data points that we can use to understand something that needs to be measured. It is data that is processed, aggregated into a manner humans could read and understand. The common ways to present information are through data visualization, reports, and dashboards.

What is Insight?

Insights are gained by analyzing data and information to understand and draw conclusions that can benefit the organization while decision making.  These are the final outputs of the data in a usable form.

How they work together

The data, information, and insight work together to yield a complete analytics package. In an organization the data is collected in a raw manner, later it is converted into a readable format that is the information, this information is later processed to insights which are highly valuable in making big decisions of the firm. If one is absent or inconsistent that means it can affect the overall functioning of the company.

Impact of Insight on Business Decisions

Data-driven marketing is the latest word that is heard everywhere, but insights are more powerful than that. Most of the brands already have data but couldn’t find the right insights. Insight can help in talking about the value of your business more than data does.

Any business decisions depend on insights not the raw form of data. These insights can make a wide difference in the strategies and performance of businesses. Great insights can help you overcome any kind of market problems such as competitors, and consumer behavior.  All kinds of evolutionary changes can occur only with the insights that can help decide better in the market.

According to Global Web Index, insights are four kinds, they are Human, Universal, True to Brand, and Targeted. In other words, a great insight reveals a story that is unique to the brands. Insights help the company and customers by coming up with new and fresh ideas.

The road to getting out valuable insights is through adequate research. Here are a few steps that can help you put information for the benefit of your business.

Goal: Set the right goal and objectives for the organization to achieve through the research. This can ensure you understand better what the company actually needs.

Collect: Once you’re done researching, the next step comes a collection of the data, whether through quantitative or qualitative.

Analyze: Here the collected data is analyzed to get valued insights. It can be about your customers, employees, products, and satisfaction.

Action: The last and final step comes where the insights are gained from analysis and these are put into action by the businesses which can help in a better decision-making process.

Now, hope you got what is the difference between data vs information vs insights and also understood how insights can impact business decisions.

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Understanding how to differentiate between data, analytics, and insights and how they work.

Data. Analytics. Insights. These three are the most important food for the soul of business today. The current scenario is more transformational and technology-dependent, where data is known as the digital currency. Data is driving business intelligence through advanced analytics and by deriving intelligent insights. These three aspects are interconnected and they build off the process of generating insights from data. Without data, analytics cannot be performed and hence, insights cannot be interpreted. However, several times people confuse these terms, using them interchangeably. This might dissolve the actual significance of the terms and what they stand for. Hence, here is an effort to distinguish them and understand their meanings interdependent of each other.

Data: The Cornerstone of Business Intelligence

Business intelligence and digital transformation sound snazzy and stylish since it involves cutting-edge technologies like Artificial Intelligence and Data Science. In order to start a transformation, you need to lay the groundwork, and data does that job. Today, there is an abundance of data that are obtained from a wide variety of sources. A singular data point might seem not useful, but when it is presented collectively, it is possible to define patterns and meaning from them. Big data is the exponentially growing huge amount of data that has become the foundation of businesses across industries. This includes data from consumers, users, clients, general audience, internet, media, and many other data points.

Businesses collect data differently according to their niche and needs. Hence, data should have a context so that meanings can be derived. Business organizations should deploy smart infrastructures and experts to collect and process the unstructured, raw data. Maintaining a data flow and storing the huge loads of data have become great challenges these days.

Analytics: Discovering the Meaning

We discussed how singular data without a context can be senseless. Analytics steps in here. It enables us to find patterns and meanings from the huge datasets and makes it sensible. Disruptive technologies have made analytics more advanced to gain better business decisions. Unlocking the real value from data is not possible without analytics. Analytics experts help in processing the collected data and translating it into a comprehensible form. With analytics, an organization can understand how it is performing over time and deliver patterns of consumer behavior and market trends.  Arriving at important marketing and business decisions is impossible without analytics.

Let us take an example of a business trying to sell a product. For this, they have to first identify the demographic they are going to serve, then find their interests and behavior patterns, establish a strategy to get the product into the market, and later measure its impacts and effects. All this is possible by deploying analytics on the data collected. For online websites, measuring traffic is a significant step today and analytics completes this demand.

Insights: Taking Actions with Decisions

Let us continue from the same example. The same product that you were planning to launch has now been provided with analytical insights. It now shows what are the unique customer behaviors, the perfect time to launch your product in the market, and highlights the risk elements involved in the process. These are called insights, or analytics insights. They are the value derived from combining data and analytics. These are powerful comprehensions that can enhance business growth, customer traction, and predict risks.

Insights are like the edible form of food you get after you add the ingredients and cook them in specific directed conditions. Here, the ingredients are data and the preparation process is the analytics performed on them.

Data-Driven Business Intelligence

Data and analytics will seem invaluable if they cannot deliver meaningful insights. Disruptive technologies like AI and machine learning play a potential role in enabling data-driven business intelligence. Insights should be comprehensible and actionable, which can improve the business and accelerate its transformation. These data-driven insights are the reason behind banks being able to detect frauds, healthcare providers being able to diagnose from medical images, manufacturers being able to smartly manage the supply chains, etc.

Data intelligence is entering into new horizons with the help of disruptive technologies providing new avenues for businesses. Today’s customer-centric businesses are surviving on the data insights to understand and engage with their customers. Businesses need to find the meaning and decisions from data analytics and the insights they derive. As the great American mathematician, John Wilder Tukey has said, “The greatest value of a picture is when it forces us to notice what we never expected to see.”

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Using machine learning methods, researchers discovered the underlying genetic contributors of cancer.

Machine learning approaches could help detect mutational signatures in patients with cancer, revealing the genetic effects of the underlying contributors to the disease, a study published in eLife revealed.

The new technique uses machine learning algorithms to access and analyze what are called SuperSigs, or mutational signatures that reveal the genetic effects of the underlying contributors to cancer.

“Mutational signatures are important in current cancer research as they enable you to see the signs left by underlying factors, such as aging, smoking, alcohol use, UV exposure, and BRCA inherited mutations that contribute to the development of a cancer,” said study leader Cristian Tomasetti, PhD, associate professor of oncology at the Johns Hopkins Kimmel Cancer Center.

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The algorithm is classified as supervised because it is an analysis that includes known exposures during the training of the algorithm for the genetic analysis of a cancer. The most widely used mutational signatures used for assessing genomic data are classified as unsupervised because they do not take known exposures into consideration. Instead, it notes patterns and then goes back to correlate them with exposures.

The new method also allows for a mix of supervised or unsupervised approaches, controlling or blocking out the effect of known exposures to carcinogens to explore the possible effect of potential unknown factors.

Researchers found that the new supervised method outperformed the unsupervised methodology in terms of prediction accuracy. The supervised methodology had a median area under the curve (AUC) of 0.73 for aging and 0.90 for all other factors, while the unsupervised methodology had a median AUC of 0.57 for aging and 0.77 for all other factors.

“A 0.5 or below AUC means the method is not better than pure chance. The highest value you can get is 1,” said first author Bahman Afsari, PhD, an instructor at the Johns Hopkins Kimmel Cancer until a few months before publication.

The team also revealed what could be the first mutational signatures associated with cancers of obese patients, providing evidence for a mutational mechanism related to obesity and the origination of cancers.

“Obesity is arguably the most important lifestyle factor contributing to cancer, but its mechanism for causing cancer has been unknown,” said Tomasetti. “As cancers of obese patients often do not appear to have an increased number of mutations, it was thought that the mechanism through which obesity increases cancer risk was not via mutations. Our results show that it is, at least in part, mutational.”

The machine learning method also showed that an etiological, or underlying, factor does not always cause the mutational effect on all tissues, a discovery that contrasted with assumptions of the unsupervised methodology.

“Aging yields different mutational signatures in different tissues, and so do smoking and several other environmental exposures,” said co-first author Albert Kuo, Ph.D. candidate at the Johns Hopkins Bloomberg School of Public Health.

“Also, in lungs, the signature for aging and the signature for smoking are very different, but in other tissues, the signature of smoking is relatively similar to the signature for aging, suggesting inflammation as the main mechanism.”

Additionally, the research provided validation for the key role of random mutations – normal mistakes occurring within the DNA of cells during replication – in the development of a cancer.

“Every time a cell divides, it has to duplicate its DNA. As the duplication and repair machinery copies the billions of letters—the molecules that make up our DNA—mistakes are made. It is estimated that there are between three to six DNA mutations occurring every time a cell divides,” said Tomasetti.

“A major source of the mutations that cause cancer appears to be these endogenous processes that have nothing to do with genetic defective genes or harmful exposures.”

With the algorithm, the team determined that 69 percent of the mutations found in cancer patients across all tumor types can be attributed to randomly occurring mutations, indicating the need for a greater focus of effort and resources on early detection.

“If we can’t avoid cancer from occurring, then the next best thing is to find it before it is too late. If we can find a cancer at an early stage, then typically, you can save the life of the patient,” Tomasetti said.

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The rapid data growth in manufacturing and similar industries has created huge pools of data which organizations can draw from to refine their processes, manage risk, and drive product innovation. Key business decisions are increasingly being made based on these big data insights. As a result, having ready access to retained data so it can be quickly and easily analyzed is becoming a higher business priority.

When it comes to data storage, many organizations often just think in terms of ‘hot’ and ‘cold’ tiers – ‘hot’ for data that must be easily accessible and ‘cold’ for data that is less frequently used. In this framework, hot data is typically kept on expensive SAN or NAS storage (flash or high-performance disk), and cold data is stored offsite on tape or in a public cloud. However, object storage provides a ‘warm’ data tier that provides quick access and is much more scalable and cost-effective than traditional SAN and NAS primary storage. In addition, object storage avoids the latency of bringing data back from offsite tape or cloud storage.

Having this warm data tier is particularly beneficial because it means organizations can afford to keep more data where it is readily accessible for analysis, which is key to gaining more actionable insights for improving operational and/or financial performance.

Keeping more data warm and easier to access

The standard approach to data tiering, where businesses would progressively move data that they need to search or access less often to cheaper ‘colder’ tiers, is becoming increasingly impractical as data volumes continue to grow. This method involved buying a new batch of storage equipment whenever there was an IT refresh, moving all the data down to the next cheapest tier, and then retiring the oldest batch of equipment.

The problem with the above method relates to the sheer volume of data now bring produced in many industries. This approach evolved in a period where there was simply much less data to store, but continually buying new expensive local storage is neither practical nor cost-effective today. Though the problem of data growth is an issue affecting every industry, in industries such as manufacturing, such growth can easily generate multiple petabytes annually – making this issue particularly troublesome. Rolls Royce, for example, can produce half a terabyte of machine data from the production of just one fan blade, with the yearly quota of production therefore exceeding three petabytes for just one component. It’s easy to see how data volumes like this can push costs to unacceptable levels.

Object storage provides massive scalability at low cost. The technology is designed for limitless growth through a horizontal, scale-out approach that allows organizations to increase capacity by simply adding nodes whenever and wherever needed. Moreover, because object storage uses a single, global namespace, this scaling can be done across multiple geographically distributed sites but managed centrally.

By incorporating object storage, organizations can better capitalize on the business advantages offered by big data, as they can store more data in a way that is easily accessible.

Advanced metadata for easier search and public cloud integration

Another key benefit of object storage is its advanced metadata features. In contrast to traditional block and file storage’s very limited metadata capabilities, object storage enables users to add rich metadata tags, making it much easier to organize, identify and retrieve data.

For example, a traditional machine log file would likely have a limited amount of metadata associated with it, such as the creation date, owner, location and size. In comparison, a machine log object can contain “user defined” metadata that provides additional searchable identifiers on the data object. This could include the originating machine’s details as searchable information, such as the machine name, IP address, and any information that’s parsable from the machine to the S3 API PUT command.

Object storage also employs the S3 API, which has become the de facto standard of cloud storage, making it ideal for integrating on-premises private cloud environments with the public cloud. This hybrid cloud capability is especially important as organizations increasingly realize that the public cloud is no magic cure-all when it comes to getting the most out of machine data.

In the case of storing large data sets in the public cloud, data access and egress fees—along with network charges for connectivity to the cloud—can be quite unpredictable and costly. Public cloud performance (i.e., data transfer time to and from the cloud) is also highly variable, as it depends on the available WAN bandwidth and the cloud provider’s overall workload at a given time. When large volumes of data are involved, the latency in accessing data from a public cloud can be significant, thereby negatively impacting business operations.

On-premises object storage offers the scalability and flexibility of the public cloud without these drawbacks or the need to rely on a third-party for data security. In addition, on-premises object storage aligns with the concept of ‘data gravity,’ in which large volumes of data are harder to move from where they’re created, making it more efficient and cost effective to store the data locally and bring compute to the data.

Unlocking the full value of machine data

Retained data is continuing to provide more and more valuable insights for businesses in every sector. But the value of machine data goes far beyond studying the behavior of machines in the short term. Machine data contains a wealth of valuable information about an organization and its processes and can provide a basis for long-term business innovation. Object storage allows organizations to retain more data by reducing the cost of keeping it readily accessible and provides other key advantages over traditional storage systems, making it ideal for uncovering these hidden gems among the noise.

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Livongo Members can now connect their smartwatches to the Livongo mobile app to receive real-time Livongo notifications on the surface of their choice enabling Livongo to provide more timely and relevant information to Members and their care teams.

Smartwatches are one of the fastest growing new technologies today and it is estimated that one in six people in the United States own a smartwatch1. These new integrations allow Livongo to offer Members behavioral Health Nudges and health information directly to their smartwatch. In addition, Livongo now offers Members the ability to sync their steps data from their Apple, Fitbit, or Samsung smartwatches with their Livongo app. This data enables Livongo’s AI+AI engine to provide more personalized activity-related insights.

Livongo’s smartwatch integration is the latest example of how the company is applying innovative technology to the Livongo Member experience. In February, Livongo announced that it will leverage Amazon Lex and Amazon Polly to power its voice-enabled cellular blood pressure monitoring system. Two months later, Livongo announced a collaboration with Amazon Alexa to offer its Members the ability to ask any of their Alexa-enabled devices to provide their blood glucose readings and health tips via the new HIPAA-compliant Livongo skill.

Our smartwatch integration allows us to capture information from our Members, add it to our AI+AI engine, and return actionable, personalized, and timely information back to them, said Dr. Jennifer Schneider, M.D., M.S., President of Livongo. By offering another way to access personalized health insights, we are able to more easily influence positive behavior change, which we know can lead to better health.

As part of the new smartwatch integration, Livongo now offers notifications for interactive challenges. These challenges are designed to empower Members to form healthy lifestyle habits that support their diabetes management and other chronic conditions in areas such as nutrition, exercise, sleep, stress management, and more. Each challenge lasts five days and is designed to enable sustainable lifestyle behavior changes that are known to drive measurable clinical outcomes by providing Members with specific and achievable goals, tools for tracking progress, and daily educational content. Examples of current challenges include incorporating walking into a daily routine or replacing sugary beverages with water. Livongo is able to anticipate potential barriers that Members might face during their behavior change journey and uses educational content to proactively address those barriers.

We are excited to offer our Members the opportunity to conveniently access valuable health information and Health Nudges using their existing smartwatch devices, said Livongo Chief Product Officer Amar Kendale. As we continue to expand our Applied Health Signals platform, we can use the integration to aggregate more important health data that we can then interpret to better understand the unique needs of our Members.

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