Category: Data Analytics

Data Analytics in the Real Estate Industry: Defeating Your Competition

Post author By Scottie Todd
Post date February 13, 2019

The arrival of real estate analytics has significantly changed how buyers, sellers and agents treat the market. The industry was once heavily dominated by hearsay and generic impressions, but many concerns that homebuyers have can now be addressed with hard facts backed by data analytics. The emergence of artificial intelligence also holds out the promise of even greater insights. Let’s take a look at how this revolution is unfolding.

What is Real Estate Analytics?

As it pertains to this industry, real estate data analytics is the practice of using computing resources to assemble large amounts of information about properties and neighborhoods. This can yield insights that no human could ever obtain due to the sheer amount of time it would take to digest all the information. Big data systems can be set up to collect information from recent sales, the MLS system, government reports and market forecasts.

This allows us to make projections regarding a variety of things of interest. For example, let’s say property investors want to know what the business future of a district in a city is. Utilizing artificial intelligence, we can develop methods for compiling available information about things like demographic trends, buying and selling habits, micro- and macro-economic developments and even individual properties and their owners. This can even be taken a step further to model the entire region over several years, providing probabilistic views of what the district will be like at specific points in the future.

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How Do We Use This Information?

Let’s say a residential property flipper wants to figure out what their exit point on a location should be. Looking at real estate analysis trends like population growth and income in a region, we can project when the market is likely to max out returns. It’s even possible to model the behavior of other flippers on the market, allowing us to guess how long pressure from their presence in the market may actually allow sales to continue to go long. By assembling these projections grounded in data analytics into reports, we can then ensure that folks directly involved in buying and selling will have a better idea of what we want to buy and when we want to sell it. It can even allow us to guess which parts of a town might be ready to heat up, permitting us to buy before the big wave hits.

If you want to know when it might be time to move in on a target property, it’ll be there in the data. You can set standards for what counts as a buy and wait for the market to come to you. Let’s say you have a strike price for purchasing a location that has been on the market for 300 days. If there’s no evidence of activity regarding the property, your analytics package can flag it, inform someone and verify that followup is done.

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Who is Involved in This?

Becoming a more data-centric operation means undertaking a massive cultural shift. Folks with math and programming backgrounds are essential to the task. Due to market-wide demand for such talent, there’s a good chance that, unless you run a massive operation, you won’t be doing this in-house.

At the same time, staff members have to be on-boarded with the cultural change. In fact, you may need to identify resistant team members who will have to be moved to other roles or retired. Over time, though, the development of a data-centric business model will put you ahead of the game, whether you’re working as a buyer, seller, or facilitator.

How to Accelerate AI in Insurance Data Analytics

Post author By Scottie Todd
Post date January 28, 2019

Mastering techniques around Insurance data analytics, knowing what data to get, and how to analyze it, greatly streamlines many of the most expensive insurance business processes.

“The United States is the world’s largest single-country insurance market. It writes more than $1 trillion in net insurance premiums every year. In emerging markets, China continues to be the growth engine.

All together, the global insurance market writes over $5 trillion in net insurance premiums per year.”

–Insurance Journal

Despite its size and global reach, the insurance business model has always been about two things.

Maximizing the premiums received
Minimizing the risk of your portfolio

Beneath these two top goals are a myriad of activities every insurance company has to master, including:

Reducing risk
Reducing fraud
Keeping customers happy with great service
Finding new customers with favorable risk profiles

Insurance fraud alone costs the insurance industry more than $80 billion per year. In an effort to overcome fraud, waste, and abuse, many companies are turning to insurance data analytics.

The staggering level of criminality costs us all, adding $400 to $700 a year to premiums we pay for our homes, cars, and healthcare, the feds say. There are simply not enough investigators to put a significant dent in the criminality, so the industry is turning to the machines.

Reducing Risk & Improving Customer Service

The insurance industry definitely has plenty of data. A single claim could have dozens of demographic or firmographic data points to analyze and interpret. A single policy could have dozens of individual attributes depending on what is being insured. Data enrichment, which has become more and more popular, can increase these data points into the thousands.

However, as insurance companies succeed and grow, datasets become increasingly large and complex. Often these are locked inside massive policy and claims management systems which do a great job of storing and maintaining the data. These do a great job for looking up individual policy records and claims, and of course, handle billing and renewals quite well.

When multiplied across an organization’s entire book of business, data sets become so large that legacy, on-premises systems are unable to keep pace with data volume, variety and velocity.

But what else could insurance companies be doing with All. That. Data?

We know when data is looked at in aggregate, surprising and valuable insights begin to show themselves.

By contrast, cloud data warehouses working in concert with Data Analytics Software make it possible to ingest, integrate, and analyze limitless amounts of data, freeing up resources to automate these important business processes:

Mastering these techniques around insurance data analytics, knowing what data to get, and how to analyze it, greatly streamlines many of the most expensive insurance business processes.

Customer Quoting, Risk and Pricing Analysis: Life insurance companies harness analytics to provide customers an expedited application and quoting workflow.

Writing Life insurance used to require multi-step risk scoring and an in-person health screening for the customer with a physician. Now it’s done almost instantaneously through the secure analysis of an applicant’s digital health records.

Fraud Detection: Property insurers use data analytics to detect and mitigate fraudulent claims. Predict fraud events from available data before it happens with a predictive analytics platform. Using Machine learning powered historical fraudulent claim data to model your risk in real-time. Look for highly predictive factors that correlate to. In this scenario, past performance is indicative of future results.

Detecting High-Risk claimants: Other algorithms can proactively monitor your portfolio and identify high risk claimants on a recurring basis, over time. After all, most claimant risk is only assessed once – when the policy is first written. However we know, financial circumstances change, properties age, vehicles require repair. Pulling together all obtainable data – policyholder financial and employment status, vehicle repair log, etc. tells companies what’s happening right now and what is likely to will happen next. This reduces manual effort and increases the effectiveness of fraud detection processes.

In about one third of cases, claims can be approved and paid out essentially instantly on approval by the company’s algorithms, he says. Even if a human is involved, it’s radically quicker. It becomes just a quick check to confirm the algorithm’s recommendation, instead of a deep analysis.

Source: Fast Company

Provider Abuse Prevention: Medicare and Medicaid make up approximately 37 percent of all healthcare spending in the United States. (according to the Centers for Medicare & Medicaid Services.) This adds up to over $1 trillion of government-subsidized hospital, physician and clinical care, drugs, and lab tests.

At these levels, the potential for waste and sometimes abusive billing by providers and health systems is always present. Program administrators and companies contracted by Medicare and Medicaid increasingly rely on insurance data analytics to combat this. This lets them identify patterns and outliers to thwart unethical billing.

Real-time Lead Scoring: New customers are the lifeblood of insurance growth. And never before have consumers and business customers had so many options and choices for insurance.

Predictive lead scoring sifts through inbound channels and optimizes leads by value and priority. Insurance Lead Scoring tools help select the best prospects with the most favorable risk profiles. Predictive lead scoring also tells insurers and brokers the best ways and times to contact prospects.

Behavioral analysis can predict whether a prospect is just shopping around or truly ready to buy. It also identifies the best method of contact for those prospects based on demographic profiling. Some prospects will appreciate a prompt phone call. Some prefer to come to a branch office. A fast growing group prefers typing over talking and responds better to a digital exchange (text messages, web and mobile apps.) Meeting the needs of these diverse audiences is the key to acquiring the best new prospects. This type of advanced profiling lets insurers predict the best methods and timing for prospect communications, and increases close and policy writing rates.

How Big Data Analytics Software in a Cloud Data Warehouse Accelerates Insurance Analytics

Unlike on-premises systems that don’t easily scale, a complete analytics platform featuring a cloud data warehouse, such as Inzata data analytics software, enables organizations to keep pace with the growing demand for insurance data by delivering:

Rapid time-to-value: Realize the power of real-time analytics to supercharge your business agility and responsiveness. Answer complex questions in seconds; ingest and enrich diverse data sources at cloud-speed. Turn virtually any raw, unrefined data into actionable information and beautiful data visualization faster than ever before, all on a single platform.

Rapid ingest of new data sources with AI:

Got a hot new leads file?

Just found out a new way to tell which vehicles will have the lowest claims this year?

Instantly add and integrate new sources to your dataset with Inzata’s powerful AI data integration. Integrating new data sources and synthesizing new columns and values on the fly can enhance an organization’s decision-making but doing so also increases the company’s data storage requirements.

The Power of Real-Time Performance: Your insights and queries are more most valuable if they get to you in time. In a competitive market where leads convert or abandon in seconds, having the speediest insights makes a huge difference. Inzata’s real-time capabilities and support for connecting to streaming data sources for analysis means you always have the most up-to-the minute information.

Make data even more valuable with Data Enrichment (One-Click-EnrichmentsTM): Enrich and improve the value and accuracy of your data with dozens of free data enrichment datasets – all within a single, secure platform.

Inzata offers more than 40 enrichments include: Geo-spatial, Advanced Consumer and Place Demographics, Political Data overlays, Weather data, and Healthcare Diagnosis Codes. Plus more than 200 API connectors to bring in data from web and cloud sources.

Security and Compliance: Cloud data warehouses can provide greater security and compliance than on-premises systems. Inzata is available with HIPAA compliance and PCI DSS certification and maintains security compliance and attestations including SOC 2, Type 1 and 2.

Real-time Data Sharing:Secure and governed, account-to-account data sharing in real time reduces unnecessary data exports while delivering data for analysis and risk scoring.

Harness the Power of Insurance Data Analytics

As insurance evolves into an even more data-driven industry, business processes that used to take hours and days are going to be compressed down to seconds. Companies who properly anticipate these changes will reap the benefits in the form of more customers, higher profits and greater market share.

Inzata is an ideal platform for insurers to take the step toward real-time, AI powere analytics that will shape the industry for decades to come.

Data Analytics

Top 5 Data Analysis Trends in 2019

Post author By Scottie Todd
Post date December 28, 2018

As businesses transform into data-driven enterprises, data technologies and strategies need to start delivering value. Here are 5 data analytics trends to watch out for in 2019…

What makes data quality management the most important trend in 2019?

The internet is on the rise and so is the availability of big data collection techniques. These big data collection techniques are facilitated by different artificial intelligence software. Over the past year it has not just been about collection of data, rather more about the quality of the data and the context in which the data is interpreted and used, which serves as most crucial aspect of business analytics. Moreover, a survey conducted by Business Application Research Centre also states that data quality management is the most important trend of 2019.

How will data lakes survive in 2019?

Not long ago, storing and obtaining actionable insights from big data was difficult. Now, with data lakes, you can store everything in a single data repository and enterprise-wide data management for everything from business analytics to data monetization. However, while storing big data in one place has been beneficial, revealing insights from that data has been difficult. In order for the data lakes to survive in 2019, it will have to prove its ‘business value’ as Ken Hoang says. This could be done by changing the ways of presenting data thereby enabling decision makers to have a deeper insight.

R Language in Data Analytics

There are a variety of ways to analyze data using statistical tools and other similar methods, but the most effective is using tools that integrate with R language. R is one of the best and the easiest way to conduct advanced data analysis since it can be audited and rerun easily, unlike spreadsheet software. It also provides a wide range of statistical techniques, making it a trendsetter of 2019.

What is cloud storage and analysis?

Cloud computing is an efficient way of doing data analytics, in fact it is a mantra one should follow, otherwise your big data faces delays if it moves across a local network and even larger delays if goes through the internet. As the amount of data increases in your computer the capacity of your data center decreases. So, in order to cater to the big data, cloud storage should be added in your computer. Along with your big data, your analysis should be in cloud too. More and more companies are switching to cloud storage as the amount of data they collect continues to grow each and every day.

Why are mobile dashboards heating up?

In this fast-paced world with everyone on the go, data management tools need to present more mobile friendly dashboards that are useful and timely for business analytics. Since many business leaders hardly have the time to sit back at their desks, this business intelligence tool is very important. Most self service business intelligence tools have this capability, but not all do; hence this should be utmost priority for every business analytics platform.

Data Analytics

How Geographic Data is Used to Make the Best Business Decisions

Post author By Scottie Todd
Post date October 29, 2018

While making important business decisions, companies, governments, and organizations are increasingly relying on geographic data to tailor their choices to circumstances on the ground. From deciding where to start new stores to determining whether groups in particular regions will respond favorably to new products, big data features heavily in the process. It’s important, however, to understand what geographic information is, why it is used, how it is utilized, and what the common applications for it are.

What is Geographic Data?

Thinking about data as it exists spread out in space often helps people – especially those who aren’t highly numerate – take something abstract and place it in a specific context. From a marketing perspective, geographic data allows you to think about the “where” factor when it comes to customers. Where are your established customers? Where is the audience you still need to reach?

Bundle together enough where-factors and you’ll soon get to the “what” questions. A company can use data analytics to ask questions like, “What is the business case for expansion of marketing in this region?”

With advances in artificial intelligence, it’s even possible to move on to questions of “who”. By developing a profile of the customers who respond well to your products, services, and messages, you can begin to ponder, “Who else matches the profile of our current customers?” With the right marketing approach, this can help a business open up new opportunities, increase revenue, and improve customer retention.

Why Use Geographic Data?

The reason for using geographic information is increasingly to gain an advantage against competitors. Firms that value data science have the potential to significantly outpace their competition. They can collect data in real time, process it through artificial intelligence applications, and present insight to decision-makers in a more timely manner.

Companies who fail to recognize the value behind the geographic details in their customer data will soon find themselves falling behind their competitors. Even for small or medium size businesses, this data is crucial for success. True success comes from growth, and healthy growth cannot come without making safe and smart business decisions by analyzing every single detail within geographic customer data.

Geographic Data is Big Data

A commitment to data analytics begins with making a choice about organizational culture. When a company wants to become a data-centric operation, it needs to onboard stakeholders, and in some case, offload those who resist digital transformation. Bringing in people who have data science backgrounds is an investment in new hiring, and it also entails purchasing the computing power they require to do their jobs well.

In terms of geographic marketing data, this approach calls for seeing almost everything as a data point to be gathered. When customers check out at stores, it’s common for companies to gather information in a variety of ways. In the simplest form, this includes asking direct questions, such as request the ZIP codes for where each customer lives. More advanced approaches include developing rewards programs that create opt-ins for tracking data about purchases and habits based on customer demographics.

Marketing Applications of Data Analytics

One of the increasingly common use cases for geographic data in the marketing world is targeted advertising. By combining opt-in data from your own sources, such as email marketing, web pages and apps, with data from vendors like Google and Facebook, you can micro-target advertisements to almost anyone who profiles as the type of customer you want to attract. If a business is attempting to identify young couples in a region where it has stores, it can utilize GPS-based data from an app to micro-target with such a level of precision that offers are only sent to them when they’re in close driving range of a physical store.

The logic of this can be taken in interesting directions. For example, a company that sells anti-frizz shampoo could target ads based on the current weather in an area. When the humidity goes up, advertising appeals can be deployed to let people know about the product at the precise moment they’re realizing they need a solution to a problem.

By developing a culture that values data science, a firm can open up new marketing opportunities. Information can be gathered rapidly, ideas can be tested swiftly, and marketing content can be updated on a minute-to-minute basis. This ultimately can make your firm more flexible in speaking to customers at times when and in places where they’ll be most receptive.

External Resources:

20 Ways GIS Data is Used in Business and Everyday Life

Big Data Data Analytics

Data Security is Crucial to Business Prosperity

Post author By Scottie Todd
Post date October 17, 2018

Why Data Security is Crucial to Business Prosperity

In the age of big data, the amount of information that companies collect is unprecedented. All that data, however, presents a tempting target for hackers. Even for intruders who aren’t interested in grabbing customer or corporate data find value in getting past data security systems in order to take control of resources on networks. Data analysis is increasingly critical to financial success in a wide range of industries, and that means that companies need to be invested in protecting their customers and themselves.

Data Breaches

We tend to think of certain organizations, such as big credit card companies, as the main targets of hacking, but the bad guys aren’t disinclined to hit a smaller operation, especially if they feel they can get quickly in and out. Worse, the good guys are sometimes the ones who cause a breach. The Boston Medical Center discovered in March 2014 that 15,000 patient records had been exposed due to a failure of a third-party services provider. Records were accessible without a password, and the data includes names, addresses and medical data. More astonishingly, the incident was the product of poor practices and not an overt hacking attempt.

A 2016 report found that only about a quarter of businesses seem to have a full understanding of the challenges they face. Even the ones that consider themselves to be knowledgeable about and tough on security anticipate continued attempts at breaching their systems, regardless of their efforts. Recovery times from breaches are expected to be at least 8 weeks per incident.

Unusual Hacking Goals

Data breaches themselves aren’t the only thing hackers are interested in these days. In January 2018, an attack on Kaseya, a company that works with managed services providers, was discovered. No information was lost because the intruders weren’t even looking for it, even though they could’ve taken whatever they wanted. The hackers were instead taking over servers to use their processing power to make money in the booming cryptocurrency market. Only third-party data analysis of activity on the systems exposed what was happening.

Attack Vectors

Hackers are increasingly hitting targets from all directions, from installing viruses to sending fake emails hoping to get someone to give them a password. Others are using automated tools to brute force their way into protected systems.

One advantage of big data systems is that they can actually be turned into tools for data security defense. Many organizations utilize artificial intelligence to study information from incoming connections to computers, allowing them to identify patterns that human security specialists might miss. These big data-driven sentries are always on guard, and they have the capacity to learn and adjust as attacks evolve.

Compliance Requirements

As more incidents have occurred, governments have begun to catch up. Extremely strict rules went into effect in the European Union in 2018 with the advent of the General Data Protection Regulation. Fines for failures of data security compliance could hypothetically reach into the tens of billions in Euros, and any company that interacts with even one citizen of an EU member state may be exposed to liability. That’s a huge risk for a shop somewhere in America to take just to make a couple more sales overseas. For those assembling true big data resources, that poses an existential risk.

Solutions

Companies need to stop seeing IT security as the domain of a specific department and start treating it as a part of an organization-wide cultural shift. All the way up to the C-suite level, training needs to be provided to ensure that decision-makers fully appreciate the challenges before their companies. Encryption of data, especially with the arrival of the GDPR and its accompanying regime of fines, should be seen as non-negotiable.

A threat response team should also be in place at every organization that collects big data information. Being able to identify and respond to attacks sooner is estimated to reduce the per customer cost of a breach by $14, according to one report. By taking a proactive approach, an enterprise can reduce its exposure and improve overall time to bounce back.

Are You Protected?

Many big data tools, even in 2018, are not as up-to-date on their security as you would expect. These tools lack security details such as multi-factor authentication, breach alerts, and much more. Your company’s data is irreplaceable and unlike any other; using software without maximum security is like throwing out thousands, even millions, of dollars. Invest in software that puts your data’s security before anything else.

Big Data Data Analytics

Data Science 101: Who, What, and Why

Post author By Scottie Todd
Post date September 20, 2018

What is Data Science, and What is its Purpose?

Big data revolves around the idea that companies can acquire and process large quantities of information in a manner that allows them to make predictions with a high probability of being accurate (i.e. a fashion purchaser might utilize data analytics gleaned from social media trends to identify what will be popular in the coming season.) This can allow businesses to get out in front of competitors as soon as possible.

The field is grounded in a set of technical and mathematical skills that are collectively called big data. Programming skills, in particular, rate highly in the industry. The three most commonly used programming languages in the business are Python, R, and Java. These are used to handle both the acquisition and processing of data.

Machine learning and artificial intelligence are also beginning to play bigger roles in the industry. Having gathered massive amounts of data, a company may lean on an artificial intelligence application to drill down through an amount that no human could reasonably read in one lifetime and generate insights. Python is by far the dominant programming language for AI applications, thanks to its seamless integration with an array of systems such as Tensorflow.

Storage is also a big deal when it comes to the business of data. The simple act of collecting information calls for massive databases, and processed information demands another layer of storage. The NoSQL database languages are popular for data analytics firms.

Who are Data Scientists?

Data scientists tend to be individuals with strong math and statistics backgrounds who also have some degree of programming ability. They can readily propose potential explanations for or solutions to problems and then devise mathematically sound tests to verify or rule out their ideas.

Many multinational companies employ data scientists, and smaller businesses are also starting to pick up on the trend. Netflix, for example, processes user data to such an extent that it even utilizes customers’ preferences to decide who to cast in shows and movies. Google employs artificial intelligence to examine emails that have been marked spam in order to do a better job of identifying future spamming efforts. Even the self-driving car revolution is being propelled by machine learning technologies designed to recognize traffic, people, animals and obstacles.

Churning through a very large amount of information is critical to the process, but good data scientists also know how to present insights to decision-makers. This includes using business intelligence platforms to show trends and predictions. Condensing all of their data into graphs and charts that can quickly be scanned and understood is what separate good data scientists from great ones.

Why is Data Science Important and Useful?

Data-driven decision-making has found a home in a wide range of industries. For organizations that don’t have the resources to go toe-to-toe with larger competitors, a dedicated & data-centric approach is a secret weapon. For a bigger enterprise, the goal is to get in front of scrappy upstarts by building their own highly competent analytics departments while also investing in the smartest data analytics platforms.

It isn’t a secret that companies in every industry are beginning to lean on data science and analytics to discover a source of power for future success. Over 90% of all of the data in the world has been collected in the past 2 years, and it is still continuing to grow. Any business that ignores this fact will fall behind its competitors…very quickly.

Data Science & Inzata

Every current great data scientist will agree that data analysis applications without artificial intelligence and machine learning will soon be outdated.

The first and only of its kind, Inzata is an AI-powered data analytics platform hosted in the cloud, allowing for optimal processing and speed. Our full service platform covers every inch of a data scientist’s daily tasks, from data ingestion, to enrichment, to modeling, and even curating insightful & readable visualizations for the rest of the company.

Inzata takes care of the tedious and pain-in-the-butt tasks of data science on a local machine, such as restructuring your data, without requiring any extra coding or data architects all at an impeccable speed thanks to our patented aggregation engine. By seamlessly fitting into existing workflows, Inzata is the only tool needed to work with your past, present, and future data scripts.

For example, if you have 3 tables that you want to combine to analyze in R, you would have 3 options:

Manually merge the tables in R and then do the analysis there (Good luck with that, you’ll run out of RAM very quickly and if you map-to-disk it will take long enough for you to watch an episode of your favorite show before it’s done)
Manually merge the tables using SQL, then export the data into R (SQL joins are also very slow and a ton of prep-work with SQL tables is necessary or else you will most likely run into a ton of technical issues, also now you need to know an additional language)
Use Inzata AI to automatically merge your tables then export the data to R in the exact structure you want in just a few minutes.

Avoiding the hours typically wasted on waiting on other programs to process data is as easy as upgrading to the most intelligent data analytics platform on the market: Inzata.[/vc_column_text][vc_column_text]Written by: Nicole Horn, Alex Durante

September 2018

Big Data Data Analytics Data Enrichment Data Monetization

The Immense Value Behind Data Enrichment with Secondary Data

Post author By Scottie Todd
Post date June 1, 2018

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 Analytics^TM, 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.

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Sources

¹Only 3% of Companies’ Data Meets Basic Quality Standards, https://hbr.org/2017/09/only-3-of-companies-data-meets-basic-quality-standards

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