Big Data Data Analytics Data Monetization

Shedding Light on the Value in Dark Data

Hearing that your organization has dark data can make you think of your data as quite ominous and menacing. Saying that data is dark, however, is closer in meaning to what people are talking about when they say a room is dark. What they mean is there’s the potential for someone to switch the light on and make what was unseen visible.

What is Dark Data?

Every operation in the world produces data, and most of those entities record at least some of it regardless of whether they make further use of it. For example, many businesses collect information about sales, inventories, losses, and profits just to satisfy the basic reporting requirements for taxes and how their companies are set up. You might also have a complete customer service department that’s producing data all the time through daily chats, emails, and many other forms of communication. Even maintaining a social media presence means creating data.

Such data is considered dark if it isn’t put to other uses. Shining a light on dark data can allow a company to:

  • Conduct analysis
  • Creating sellable data products
  • Learn about relationships
  • Supply insights to decision-makers

By definition, dark data is an unutilized resource. Owning dark data is like keeping things in storage that never or rarely get used. In other words, if you tolerate the existence of dark data within your organization, you’re at risk of leaving money on the table. In fact, you may be taking a loss on dark data because you’re storing it without first turning to monetization.

How to Bring Dark Data into the Light

The first order of business is figuring out exactly what your organization has in the way of data sources. Some things will be fairly obvious, such as turning up sales data from a POS and inventory numbers from an ICS. Other data sources may be trickier to find, but they can be discovered by:

  • Surveying your team members to learn what data different departments collect
  • Conducting audits of computing systems to identify databases, log files, and spreadsheets
  • Scanning through social media feeds, including direct messages from customers
  • Collecting corporate data, such as financial statements and email correspondence
  • Studying call records

It’s also wise to think about places where you could be collecting more data. For example, a customer service system that isn’t sending out surveys is letting a perfectly good opportunity go to waste.

What to Do Now That You See the Data

The second order of business is figuring out how to draw more insights from your data. Companies accomplish this by:

  • Creating data lakes and providing access to them
  • Auditing databases for potentially useful information
  • Implementing or expanding data science projects
  • Developing data-centric corporate cultural practices
  • Adding resources to do machine learning and stats work
  • Hiring new professionals who can dig into the data

Much of this hinges on moving forward with a data-centric culture. Even if you already feel that you have one, there’s a lot to be said for looking at who your team members are and how you can use dark data with experienced data users at your disposal.

The third order of business is establishing goals for your projects. If you run a company that has potential legal risk exposure due to compliance problems involving laws like HIPAA and the GDPR, for example, you might analyze the very way your organization stores information. A company that collects huge amounts of anonymous data from millions of users might figure out how to package that data into sellable information products, such as reports or datasets. You may even cut costs by determining what data is useless, potentially removing terabytes of information from storage.


Modern organizations collect so much data that it’s hard for them to clearly imagine what they have at their disposal. It’s important to take an extensive look at all the ways dark data may be residing in your systems. By being a bit more aggressive and imaginative, you can find ways to improve processes, cut costs, and even drive profits.

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Data Monetization

New Report Shows Big Data Plays A Key Role In Improving Driver Safety

Big data is often used to make the world a safer place. We can use big data to develop better predictive analytics tools to identify risks and take the right precautions. One of the best examples is using big data to protect driver safety.

Companies that use big data can create better contingency plans. They will make sure that the right measures are in place to avoid the risk of injuries and deaths on the road.

The Role of Big Data in Highway Safety

Car accidents are the cause of 1.25 million deaths per year, with an additional 20 – 50 million injuries or disabilities relating to automobile accidents. Big data is being mined to be able to improve driver safety.

But how?

Predictive Analysis and Crash Maps

Tennessee conducted a crash prediction program in 2013 that analyzed crashes based on reports, traffic conditions, and weather for specific 6-by-7-mile-wide areas. The data was used to create maps that officers and highway patrol used to create safety checkpoints.

New enforcement plans were put in place so that officers could patrol in areas where accidents were most common.

Crash response time dropped by 33% and fatalities fell by 3% as a result. This was an impressive set of results. The other benefits shouldn’t be ignored.

Big data can also help cities understand their road usage and risks. I-80, where it connects to US 395 in Nevada, was designed to have 90,000 vehicles on the roadway per day, but rapid growth in the area has led to more than 260,000 vehicles per day on this road. Cities can use big data to predict how traffic will increase, offer better maintenance and expansion plans, and generally increase safety on congested roadways.

Predictive analysis helps officials take action so that they can lower the risks of accidents and help decrease response time to accidents.

Autonomous Driving Enhancement

Autonomous driving will be able to increase the safety of drivers, and it is big data that will help push this technology to the mainstream. We’re already seeing vehicles that can use blind spot detection or apply the brakes based on the actions of vehicles ahead of the driver’s vehicle.

With 10 million self-driving vehicles expected to be on the road by 2020, these vehicles should help reduce some of the $871 billion that car crashes cost the economy each year.

Telematics to Coach New Drivers

New systems are already being developed to capture data in real-time and be able to sift through big data to analyze a driver’s behaviors. The power of data will be used for telematics so that new drivers can effectively be “coached” on how they drive.

Harsh braking habits, rapid speed increases or even speeding can all be logged and analyzed.

The systems will rely on big data to better help new and seasoned drivers understand their driving habits. Similarly, trucking fleets and other commercial transportation fleets will be able to use telematics to keep a close eye on their drivers.

When telematics is in use, reports can be made and actions can be taken to curb bad driving behaviors.

Data can also take into account a driver’s actions. The idea is that the vehicle will be able to function differently, based on the driver’s actions, so that the vehicle’s braking or acceleration is altered. Big data can also help eliminate speeding, or it can be used to determine whether a driver is wearing their seatbelt. In the latter case, the vehicle will force the driver to put on a seatbelt before the vehicle starts.

As big data continues to be used, vehicle safety will improve.

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Data Monetization

Your Company’s Financial Data Has Immense Hidden Value

Using financial data to mine for insights, present reports and handle other critical tasks has become a major reason for the popularity of computer-driven analytics. That has led to immense competition, and consequently, there is immense value in digging through financial data to find what may be hiding. Let’s take a closer look at what financial data is, how it is used and what hidden value may be lurking within it.

What is Financial Data?

Financial data generally refers to information that can be derived from accounts and securities. The most basic forms of financial data include things like cash flow, net income and total assets. Notably, the idea extends far beyond those three items, but these three provide the most accessible way to understand what financial data is. This information is useful in:

  • Providing credit to individuals and companies
  • Establishing buy and sell points on stocks
  • Financial planning
  • Placing valuations on businesses
  • Determining interest rates
  • Forecasting future economic conditions
  • Detecting misrepresentations and fraud

In other words, you likely interact with lots of financial data on a daily basis even if you’ve never invested a dollar.

How is Financial Data Used?

Utilizing financial data is increasingly about feeding information into machines. For example, credit card companies regularly monitor transactions worldwide to detect patterns of theft, fraud and misuse. Your bank account might be flagged because your ATM card was detected in use in a geographic location you’ve never been to.

Analysis of financial data is performed using an array of mathematical, statistical and programming tools. The loan officer assigned to determining whether you might get a mortgage may use a computer model that compares your financial situation to similar customers to assess what your risk of default is. That requires access to large datasets, and it’s essential to have enough processing power to make the comparisons rapidly enough for them to be relevant.

Sources of data are also quickly becoming more diverse. Where financial data was once limited to banks and stock traders, we’re seeing actionable information come from previously unthinkable sources. For example, a mercantile exchange trader may gather data from farmers in the Midwest to determine whether crop yields will be up this summer. Similarly, farmers can become sellers of their data, transmitting information from IoT sensors placed in field to co-ops that then monetize the data by selling it to traders.

Where is the Hidden Value?

Data is rarely is a true representation of a thing in the real world, and that means getting at what might be hiding requires some tricks. For example, Bayesian analysis is frequently used in assessing medium- and long-term risks in stock markets, bonds and other financial instruments. Traders configure their models and buying programs according to a wide range of variables, including their comfort with risks and how concerned they might be by potential shocks in the market.

Differences in how analysis is done mean there will always be parties with different opinions of the future. These differences are often referred to as market inefficiencies, and much of the hidden value in financial data lies here. For example, there will always be differences in the estimates of the core brand values of different products. Someone using social media analytics might identify an old brand that’s making a comeback and buy into its parent company’s stock to leverage that advantage. That’s hidden value.

It takes time to become familiar with the tools and techniques used to assess financial data. With time, though, you can utilize it to begin maker better decisions about assets, liabilities and risks.

Data Monetization

Retail Data Monetization: What, Why, & How

What is Retail Data Monetization? 

Retail data monetization is the process of using your companies transaction and customer data to optimize the way you make and spend money.

There are two different ways to monetize data:

  • Direct monetization
  • Indirect monetization

Direct data monetization is when a company sells their data to another party. This can be done in many ways, such as in a package (ex. Data from 2015-2016), or as in giving access to a live feed of data as it comes in.

Indirect data monetization is where it gets more complicated. This is when a company uses their data to optimize their business strategy to be the most profitable. This could involve finding a cheaper way to do things, or using data to find among which demographic your product is the most popular and target your advertising more toward people who fall under that demographic.

Either of these processes can be applied to retail data monetization.

Retail Data Monetization: Why Should You Do It?

There are two main ways data is monetized indirectly. The first way is using data for cost reduction by increasing productivity or reducing consumption/waste. The second way involves using the data to improve sales or strengthening the customer base. 

For example:

  • Use data to geo-target retail for specific locations
  • Traffic and density planning for agencies, such as: advertising, government, transportation, city planning, and  healthcare
  • Detecting fraud in financial institutions and credit/debit card companies
  • Targeted ads based on click insights for brands and advertisers
  • Layout, location, and staff planning for retail stores
  • IoT (Internet of Things) applications for a wide assortment of companies

As you can see, there are many many uses for data in any business, and especially so in the retail industry. Retail data monetization is becoming the next big thing for any and all retail companies to invest in, and if they want to get ahead or stay ahead, they should make it a priority to utilize within the next couple years if they haven’t already.

Retail companies should realize that one of their best products, and most valuable assets in today’s world is data and data analytics. In 2015, the size of the retail analytics market was estimated to grow from $2.2 billion to $5.1 billion by 2020, with an estimated Compound Annual Growth Rate (CAGR) of 18.9%. 

How to Start Monetizing Your Data

Here are the 6 major factors you and your company should evaluate:

  1. Usage Rights: Do you and your company have the legal rights to re-use and sell your client’s (B2B and consumer) data?
  2. Readiness: Does your company have the required infrastructure? (IT, Sales, Service)
  3. Privacy: Do you and your company understand the privacy regulations pertaining to this data?
  4. Value Proposition: Do you understand how to value the data accurately in order to create a fair but profitable price for both you and your consumers?
  5. Timing: Are you a first mover among your market’s competitors? Are you at least in the first 50%? 
  6. Market Share: How much of the market does your organization control?

Before a company can start monetizing their data, they need to upgrade their foundations. This includes their strategy, design, and architecture. It will help them build their platform to begin monetization effectively. 

Data analytic companies can be externally sourced to provide help outside of existing capabilities. They may host tools or data providers that can get access to unique sets of data that may be otherwise unavailable. 

Many companies struggle to monetize their data to its fullest extent, only about 1 in 12 companies do it correctly. By finding the right strategy, and delegating the efforts to be focused on the more valuable use cases, a company can gain access to a whole new source of income that many businesses have yet to discover.

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Big Data Business Intelligence Data Analytics Data Monetization

7 Ways To Grow Your Business with Data Monetization

It’s estimated that by the year 2020 revenues around the world for big data and business analytics are going to exceed $203 billion. With all this earning potential, it makes sense to want to get your business “in on it.”

One of the best ways to do this is with data monetization. After all, data is the new currency.

In the past, businesses in the information technology sector have always been deriving value from data. However, the ability to effectively use and monetize data is now impacting virtually all types of business.

This means that driving value from data is something you can implement in your own business strategy. What many people may not realize is that this process can be extremely challenging.

As a result, you need to learn some helpful tools and actionable steps you can take to monetize data for your business.

If you are interested in learning more, then keep reading.

1. Decision Architecture

When thinking about analytics, the majority of organizations want to know how their business is performing, along with what information is needed to answer various performance questions. While this can help to inform and to describe what is taking place in the organization, it doesn’t enable any type of action.

Instead, the goal needs to be to capture the decision architecture of specific business problems. Once this is done, you can build analytics capabilities to create a diagnosis that enables decisions and actions. Leaders need to focus on making decisions that are based on data, rather than just answering questions about what already happened.

2. Stop Revenue Leaks

Busy healthcare providers, clinics, and hospitals can easily lose track of the services being rendered. Every procedure has an assigned code and description. Each of these often includes errors.

By using analytics, the organizations can identify patterns associated with procedures and codes, flagging patient invoices for possible errors or even missing charges. Intelligent data use can also help the organizations improve the ROI of their collections process.

3. Data Aggregation

The method that is at the very bottom of the pyramid, but that represents the biggest opportunity to earn, is data aggregation.

This means taking data from various sources, including your business, and merging it together to create a larger, integrated picture. While the data sources on their own may be interesting, when they are combined, they become valuable.

An example of this would be your credit report. The information credit bureaus aggregate, such as the credit cards you have, if you have a mortgage, and if you pay your bills on time, can be sold for a profit.

By aggregating this information into a single report, the information can be sold to interested parties. While there isn’t a lot of money in this, it’s still money.

4. Infer Customer Satisfaction

Many organizations use social media and survey sentiment to understand the levels of customer satisfaction. By combining data from several sources, airlines can now infer how satisfied a customer is based on factors, like where they are sitting.

This process requires information to be aggregated from several sources. However, in the airline example, you can use the information to determine if a customer is going to fly with you again, and if not, offer a free upgrade or other incentives.

5. Embrace a New Revenue Model

Today, data is actively changing relationships companies have with customers. Manufacturers of tangible goods are now supplementing the products they sell with flexible software options and services to offer customers new choices and new revenue streams.

Additionally, these companies are providing much higher levels of personalization. Across several industries, new economic models are starting to be explored – like replacing an auto fleet with self-driving cars.

In this example, rather than selling data, people are going to pay you to solve a problem or to provide answers. This is a unique revenue model.

The value lies in the fact that you have married your data to the mission of a business and solving a problem that businesses have. This is what is going to generate revenue.

6. Detect Piracy and Fraud

Most online retailers sell products on several different websites. Supplemental sales channels typically include eBay, and other online marketplaces maintained by larger retailers, like Best Buy and Walmart.

Selling through these channels is extremely data-intensive, since the customer types, products, and pricing can vary greatly across the channels. In some case, the price discrepancies are so large that they signal possible piracy or fraud.

If you sell across dozens of e-commerce websites, then consider building databases of your own products and your unique pricing. You can then compare this to existing expected pricing data, allowing you to detect stolen goods or suppliers who are mispricing their goods.

With this information, it’s possible to go to the marketplace and make a report stating that they believe someone is selling stolen items.

How Can You Use Data Monetization Methods for Your Business?

Data monetization is an ever-evolving concept that offers opportunities to earn profits by providing information to others. Your business can take advantage of this by utilizing the tips and information here.

The fact is, there are already countless businesses, in all industries, that are currently using data monetization. Now is the time to begin doing so, too, as it offers huge revenue stream potential.

If you are convinced that data monetization is something you want to use for your company, then contact us. We can provide you with help and information about how this process works.

Data Monetization Insurance

Monetizing Your Data in the Insurance Industry

“One way to future-proof a business in the insurance sector is to lean on data monetization software.”

As the insurance industry changes alongside a number of social and technological trends, many companies are looking for ways to improve their bottom lines using data analytics tools. One way to future-proof a business in the insurance sector is to lean on data monetization software. You may be wondering, though, what exactly data monetization is and how you can put it to work.

The What of Monetizing Data

Data analysis can be performed using a number of resources that most insurance providers already have access to. The industry is demanding in terms of the amount of data that is taken in from customers themselves and from incident reports. This offers a lot of opportunities to go into a data lake and derive insights that may help a firm operate more efficiently, reduce risks and properly priced products. You may be interested in conducting:

  • Fraud detection
  • Loss prevention
  • Predictive modeling of macro-scale risks
  • Analysis of customer relationships

One major advantage that most insurers have over companies in other sectors is they tend to own huge repositories of historical data. When working with analytics, it’s impossible to over-emphasize just how much value comes from feeding more information into any data monetization software system.

The Why

If your company is curious about the potential impact of new safety features on automobiles, for example, you can make comparisons to historical precedents. This can include looking at moments like the advent of seat belts and airbags, to get a sense of what the risk profile of your average customer will look like in 5, 10 or 20 years. While this sort of modeling isn’t considered purely predictive, it provides a starting point for understanding changes that are hard to plan for.

Fraud detection is also a major opportunity for monetizing data. In the modern business environment, many people engaged in fraud are working together either directly or sharing information across the internet. This means that new kinds of fraud can appear seemingly out of nowhere. Likewise, individuals engaged in fraud may move around. By looking for patterns in how they purchase insurance and file claims, it’s possible to identify both buying and filing behaviors as they’re just appearing.

The How

Acquiring the staff and building out the infrastructure required to perform meaningful data analysis requires a significant shift in a company’s attitude toward computing. Data science places an emphasis on testing various hypotheses, and that means you’ll need team members who have strong backgrounds in statistics in order to assess the relevance of output from your data analytics tools. This goes beyond the basic actuarial work that’s done in the insurance world and extends into other disciplines, including computer programming, economics, pattern recognition, social sciences and even psychology.

All this work is underpinned by significant amounts of computing resources. In particular, companies need a lot of data storage capacity to provide robust enough databases for analysis work. This entails installing servers, setting up redundancies and providing reliable networks for both machines and users to communicate across. In some cases, a high-speed network may call for completely re-cabling buildings to ensure the infrastructure is robust enough.

Culture Change

Establishing a culture that values data and analysis is also critical, and it demands more than just bringing in stats geeks, IT people and computer programmers. From the bottom to the very top of your organization, stakeholders need to be on-boarded with the culture change. This includes training sessions where decision-makers are taught about data dashboards and what their contents actually allow them to do.

Likewise, training needs to include education about the power and limitations of data. The insurance industry has many privacy issues that have to be broached. There also needs to be an understanding that excessive reliance on computer-driven answers can create its own set of problems.

One downside to this approach is that some people are going to resist change. New assignments and severance packages need to be available to ensure that folks who can’t follow the company into this new era aren’t left in positions where they can impede progress. Hiring processes should also be altered to ensure that new employees show up ready to be part of a data-centric business culture.

The culture change toward data analysis is a long one that calls for commitment. It takes time to bring in skilled professionals to set up systems and make choices about what processes need to be used. Similarly, stakeholders need to be patient in order to allow the benefits of monetization of data to begin to flow into the company. As the culture shifts and processes are refined, though, you’ll begin to see a discernible uptick in profits. 

Data Monetization

Why Should I Monetize My Company’s Data?

Taking advantage of big data systems is a challenge that many companies are just beginning to confront. Within these efforts are serious questions about how data monetization can be done to increase revenue. It can be helpful, however, to think about what exactly data monetization is and how data analytics can be employed to turn a profit.

What is Data Monetization?

Most companies have the capacity to collect information about their customers, marketing efforts, operations, etc. From the data that’s collected during registration of products to information gleaned from shifts in inventories, a lot of organizations are churning through significant amounts of data, regardless of whether they’re truly taking advantage of it. When those efforts become focused on turning a profit through re-selling their non-private data, that’s when it becomes a monetization effort.

The How

A business has to develop a commitment to collecting and using big data. Information can be culled from a variety of inputs, but the critical thing is that the data then be stored in databases, processed through a data analytics platform, and organized in a style that is able to be presented and sold. This means developing a process that can handle the amount of data and an internal culture that seeks the connection between big data insights and revenue, i.e. the goal of any data monetization process.

Should I Sell Data?

The simplest business case for data monetization also happens to be one of the most difficult for companies to use: selling data. While it’s straightforward from a conceptual standpoint, it ends up being the most challenging because sellable data has to be something that can’t be found elsewhere and you need to have an insane amount of it. For a company like Facebook, which has built its entire business model on selling data, that’s fine. For a firm that’s established in a different sector, such as retail or healthcare, it may simply be prohibitively difficult to pull off. There are also frequently ethical and legal concerns that accompany such a business model. 

Stopping Revenue Losses

At most operations, there’s a sense that money is being lost, but putting business processes in place to prevent those losses isn’t always simple. One advantage of leveraging big data at a company is that it allows you to scan through a large amount of information to try to find patterns that humans would either never recognize or take years to identify.

In the healthcare world, for example, segmentation is being increasingly employed in the billing and collections processes. A hospital’s collections department might determine, based on what it has learned from data analytics, that a segment of the population is highly unlikely to respond to a phone call about an outstanding bill. They can then divert resources toward seeking collections from patients who are more likely to answer their phones and agree to pay their bills. Similar approaches can be used by companies to deal with fraud, piracy, counterfeiting and theft-of-services issues.

This kind of information is what other companies are willing to buy. They see the value of investing in information that will prevent a decrease in revenue while also avoiding discovering such insights themselves. Essentially, they will spend money to avoid losing even more money, and time.

Selling Answers

A quality big data operation can become an asset in its own right. If you have data scientists in place and people already generating insights, you can sell those insights as products. In the financial sector, we see major players like Gartner regularly selling the answers they’ve gleaned from their existing efforts. Being able to get insights out of your data is more valuable than being able to collect and process it, and others who’ve struggled to complete that final step will often pay well to not have to bother with it themselves.

Changing Customer Relationships

Just as customers can be segmented to reduce losses, they also can be segmented to drive growth in sales. Many retailers have found, for example, that potential repeat customers are often just waiting to be given the right offer. If you have an email marketing list in place, you can test different offers and analyze who responds to which pitches. In fact, many websites have turned this into their main business model.

This is another example of data that is worth monetizing. If a retailer has customer purchase behavior data, there is no doubt that it is extremely valuable to another non-competing retailer.

In Conclusion

The amount of time and resources that goes into collecting any type of data is something of value. It is human nature to prefer to pay for something to be done, rather than doing it yourself, at least when it comes to something so tedious. Companies that have spent years and years gathering big data, whether it be from customers, products, services, operations, etc., could possibly have a monopoly on extremely useful information that other companies are willing to purchase.

With the right data analytics tools, big data can be monetized in minutes. Are you investing in your big data?

Big Data Data Analytics Data Enrichment Data Monetization

The Immense Value Behind Data Enrichment with Secondary Data

Techopedia defines data enrichment as “processes used to enhance, refine or otherwise improve raw data.” Raw data is just the seed, and data enrichment is the light needed to grow it into a strong, useful, and valuable mechanism for your business.

Ultimately, the goal of data enrichment is to boost the data that you are currently storing with secondary data. Whether it is at the point of capture or after the data is accumulated, adding insights from reliable information sources is where the real value is gained. In other words, data enrichment is journey of transforming your raw, commodity data into a true asset to your organization, project, or research.

Refining raw data should include the following steps:

  • Removing errors such as null or duplicate values
  • Using data profiling to clarify the content, relationships, and structure of the data
  • Improving the data quality overall to increase its reliability and analytical value
  • Strategically adding additional attributes, relationships, and details that uncover new insights around your customers, operations, and competition from secondary data

Data refinement avoids the negative outcomes of attempting to work with bad data. Low quality data can have serious negative impacts on your project. It can needlessly increase costs, waste precious time, cripple important decision making, and even anger clients or customers.

During or after the refinement of your data, enriching it with advanced data dimensions such as detailed time frames, geography details, weather history, and even a wide variety of customer demographics from multiple secondary data libraries is key to unleashing its true value to your company, customers, and shareholders.

  • What if you could predict which clients are most likely to buy, and exactly how much they will spend, just from their initial lead profile?
  • What if you could identify the key success characteristics of a new market or store location, just from viewing the demographics of the area?
  • How much easier would day-to-day decisions become if you could consider all of the factors involved, instead of just a few?

You will acquire a better and more complete understanding of your prospects and target market. You will learn more about your market by appending business information to the records that you capture and store, pinpointing key sociodemographic groups of business prospects, or improving efficiencies across your business units.

Most would agree that data enrichment with secondary data is valuable, but why do less than 10% of companies do it? The simplest answer is “it’s hard.” It’s time consuming and labor-intensive to gather and maintain all of these various enrichments. It’s hard to thread and blend data together AND keep it all accurate and organized. Let’s face it, most business professionals barely have time to analyze the data in front of them, much less go out and find other sources.

Let’s Talk About Inzata

Inzata is a data analytics platform designed to change all of that. Inzata offers a growing list of more than 25 separate enrichments, ranging from things like geospatial and location enrichments, to weather data and advanced customer demographics down with street level accuracy.

Data enrichment is a core function with Inzata, it’s designed as an integral part of our Agile AnalyticsTM, the workflow that uses technology to turn raw data into digital gold. 

Secondary data is the key concept of data enrichment, such as advanced customer demographics, which is arguably the strongest data enrichment a company could use to add an immense amount of value to their customer data. Unlike any other data analytics platform, Inzata has over 150 customer demographics from the entire nation built right into the platform for one-click access at all times. Some of these enrichments include:

  • Income brackets
  • Employment
  • Occupation
  • Housing occupant/valuation
  • Marital Status
  • Education level
  • Industry facts

Enriching your customer data in this way greatly increases the value and precision of your analysis, and allow you to answer much more complex questions about your business. Inzata makes enriching your data as simple as selecting which attributes you want to add, and instantly adding them to your data.

These enrichments are absolutely priceless for companies with big data on their hands. Being able to slice and dice your large datasets by these detailed demographics and behavioral characteristics makes them more precise, more manageable, and better able to tell you what’s actually going on inside your business. Think of enrichment as a force-multiplier for your big data initiative. Knowing more about your customers, your transactions. Failing to enrich a mass amount of simple customer data for your own benefit is like choosing a 2005 flip-phone over a 2018 smartphone.

A Harvard Business Review1 article mentions two very important statistics that easily prove why data enrichment is absolutely crucial:

  • On average, 47% of newly created data records have at least one critical & work-impacting error.
  • Only 3% of the data quality scores in their study can be rated “acceptable” using the loosest-possible standard.

Any business can easily avoid falling into these negative statistics by investing in the correct data analytics platform that provides powerful enrichments for top-notch data refinement and enhancement through a variety of secondary data sources.

Inzata’s platform is the first and only of its kind to include one-click enrichments for any data, from any source, for any business. Stay ahead of the curve in data analytics and invest in the best, invest in Inzata.


1Only 3% of Companies’ Data Meets Basic Quality Standards,

Big Data Data Enrichment Data Monetization

The Chief Data Monetization Officer: Turn Big Data into Profit

Humans produce around 2.5 quintillion bytes of data daily. However, over 90% of data collected is never read or analyzed. Data monetization is the process of putting your data to work, resulting in economic benefit.

In many businesses, the amount of data that goes unanalyzed is much higher, approaching 100%. We’re spending millions to collect and store this resource, but we’re only putting a tenth of it to practical use. That’s like finding a massive oil deposit underground, and just pumping the crude up to the surface and storing it in huge tanks.

So the problem is not that there isn’t enough data. We have plenty of data, and exceedingly good at collecting and making more.

The problem is one of refinement and distribution. Monetizing oil requires refineries, trucks and gasoline stations to get it to market. Without those, the oil is worthless.

Big Data is not of much value unless it’s driving profit and positive change in the enterprise. Once you’ve figured out how to do that, its value skyrockets.

The one big difference between data and oil is that you can only refine oil into a product once, then it’s gone. Data stays around. You can keep monetizing the same data over and over by refining it, analyzing it, combining it, and produce valuable new assets over and over.

The right insights at the right time can be priceless. They can save lives, avert disasters, and help us achieve incredible outcomes.

Great data projects start with great questions. Not “interesting” or “nice to have” questions, but truly great questions that, when answered, will visibly move the needle on the business.

Unfortunately, most business leaders aren’t used to walking around the office asking impossible questions that seemingly no one can answer. But that’s exactly what I encourage them to do.

The most valuable person at the start of any Big Data project is the person who understands what’s possible with Data Monetization. It takes vision, and their confidence gives others the courage to ask the hard questions.

It’s not enough to just collect and work with data. The questions don’t come from the data, the answers do. It’s your job to come up with the best questions.

Organizations across all industries have large volumes of data that could be used to answer consumer and business questions or drive dta monetization strategies.

This requires a skill many organizations have yet to develop. To get the maximum economic value from data monetization, organizations should shift their emphasis from Chief Data Officers, or CDOs, to Data Monetization Officers.

Low-cost BI analytical platforms are revolutionizing the way the world makes decisions. A bold claim? Not really. To help us examine the impact of widely used BI platforms with Big Data will have, let us describe how the information sharing and data monetization process works.

Chief Data Officers typically come from an IT background and report to the CIO. A DMO comes from a business background and understands how the business functions the way a COO or CFO would. They’re tasked with using data to provide direct, measurable benefits to the business. Their job is to monetize the company’s information assets. They have an inclination toward revenue growth and are skilled in finding new data monetization revenue opportunities and customers.

The DMO has a strong affinity for measurement. This shouldn’t be much of a stretch for someone with “data” in their job title, but they need to be willing to apply it to themselves as often as necessary. They need to be picky in choosing the truly “great ideas” for data monetization. They need to resist the ones that won’t improve business performance, no matter how neat they sound.

Smart organizations understand the benefits of having someone focusing on extracting business value from data and charting ata monetization strategy.

By 2022, most companies will have a specialized resource, or DMO, in charge of managing and monetizing the company’s most valuable asset: its data.

If you’re reading this thinking “We don’t have enough data to justify this kind of role,” Think again. Most companies already have more than enough data to make an initiative like this worthwhile.

I’d love to know what you think.

Would your company benefit from someone in charge of managing the ROI of data?

Could dataetization change the way you look at your data, and possibly create opportunity for profits?

How effective is your organization at leveraging data and analytics to power your business?

Are you a candidate for this type of role?

  • Do you understand your organizations key business initiatives and what data reflects how they are doing? Do you understand and track leading success?
  • Can you estimate the economic value of your data both inside and outside of your company?
  • Do you have the skills and tools to exploit this economic value?

Learn more about Inzata, the first Analytics platform designed for Data Monetization.

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