Big Data Business Intelligence

6 Information And Analytics Trends To Prepare for in 2020 

We are well past the point of realization that large data and advanced analytics solutions are valuable — nearly everyone knows that by now. Actually, there’s no escaping that the increasing dependence on technology. Substantial data has become a modern staple of nearly every sector in retail to manufacturing, and for good reason.

IDC forecasts that if our digital universe or total information content were represented by tablets, then by 2020 they would extend all of the way to the moon over six times. That is equal to 44 trillion gigabytes, or 44 zettabytes of information.

There are lots of reasons why information has been created so quickly — doubling in size every 2 years. Although the demand for reliable information is another the birth of IoT and connected devices is one source. What’s more interesting are the tendencies formed as a result of the options that are digitally-reliant that are more recent. They specifically help form the business, altering business analysts work with information.

Will our future look like? How will we handle all this information? What abilities need to company analysts be focused on developing?

1. Specialization of Job Roles

For quite a while, the information scientist and analyst roles have been universal in character. It is not that specializations did not exist, they have but firms are starting to search for professionals who have industry-specific experience. They want someone well versed specifically in the sort of data they are dealing with.

Everything from financial services to manufacturing and logistics has been upgraded to rely on more digital services and as a result a influx of real time information. There are plenty of opportunities, so livelihood decisions won’t be hurt by picking a specialty, but doing exactly the can. It is important to construct a good CV by working together with businesses and teams that fit a specialty, so choose one.

2. Machine Learning Experience is a Must

From 2020, over 40 percent of data science tasks will be automated. Its capacity and machine learning technologies is a massive driver of that automation. It is for good reason because effective machine learning tools and automation will help extract insights that would otherwise be tricky to find even by skilled analysts.

The whole process is achieved much faster, boosting not just general efficiency but the response time of an organization to certain events.

Data scaling analysis, quantitative analysis, automation resources and, of course, overall machine learning are skills that modern data analysts must try to hone. Talent and the more experience that an analyst has with automation technologies, the more desirable they will be.

3. The Growth of Legislation

GDPR helped to spur the requirement for qualitative information governance, and frankly, it happened so quickly it left many firms scrambling to comply — even still some are fumbling with the idea. But it is not. More lately, that the California Consumer Privacy Act reared its head, that will go into effect in 2020. It won’t be the last either, not by a longshot.

These regulations have a monumental effect on information processing and managing , customer profiling and information security. Businesses are under extreme pressure not only to comprehend the effect on operations but also to comply with the requirements.

Data scientists and analysts who understand the ramifications will help organizations browse the guidelines and are skilled in security and data privacy are in large demand. As regulations come to be, that need will continue to rise making it a viable specialty for current and future professionals.

4. Stay close to the Bleeding Edge

It’s no small accomplishment to stay up-to-date with anything that relates to modern technology. Tools and solutions are growing at absurd prices, new opportunities are being introduced, and many different tendencies take form year after year. But regardless of how difficult it is, information analysts have to continue to stay in the forefront of that growth.

A good analyst may focus but never puts their whole stock in toolset, platform or one technology. Using databases, as an instance, choices may include HBase NoSQL and MongoDB but its most priorities may change over time. Information processing is another skill key to remaining relevant in the analytics area. Professionals will probably be desired by companies, individuals and government offices .

For frameworks and languages, there’s Apache Hadoop, Python, R, SAS and others. However, more importantly, these would be the cases now — right now — and they could shift or alter over time.

It is up to analysts to stay present with all and any options readily available, and that means embracing a state of growth and constant improvement so far as knowledge and abilities are involved.

5. Cloud Computing and Related Mechanics

Software engineers and data scientists are two fields, but that does not necessarily imply overlap does not happen. It certainly does and professionals need to understand that achieving it is a very important part of staying relevant in the market of today.

As the requirement for more liquid and elastic infrastructure develops, scientists and analysts need to comprehend how this relates to current operations and gear. When dealing with possible troubles and performance demands Having the ability to evaluate the load on a machine, for instance, can go a very long way.

It is this concept of knowing infrastructure and the hardware in the helm that will elevate professionals to fresh heights. Substantial data analytics, machine learning, none of these technologies would exist with no cloud computing along with the infrastructure.

Until recently, the focus has been about processes and the instruments that can help attain a better understanding of information stores.

The technology gets more capable and is adopted more publicly, as, the requirement to comprehend the hardware has also become more important. The overlap between software and hardware related jobs and the demand for professionals to comprehend the range of the systems.

6. Basic Business Intelligence Expertise is Key

The data analyst of today kept separate and then is not secured in a tower. In fact, it’s nearly always the opposite that is complete, as scientist interact with decision makers and groups. This means that information professionals have to be able to efficiently communicate complicated issues to professionals.

Communication occurs to be a critical soft skill of company intelligence. But it’s not the only skill required to thrive. SQL programming abilities Tableau such as — and problem-solving are just a couple of examples.

The past and tomorrow’s analysts should have a good foundation in business intelligence.

Growth is Always a Must, however the Right Growth is Key

Obviously, build experience in the industry and information analysts may continue to rise as they take on more projects. But boosting the growth that is perfect, into particular areas and abilities, can assist professionals achieve victory, but also secure opportunities in the area.

An increasing number of organizations deploy information analytics tools to affect their operations and to understand consumer behaviour.

These tools have become more advanced alongside the technology, as time progresses. It is around analysts to comprehend tools and the core systems but also the underpinning hardware

Big Data Data Analytics Data Quality

What You Need to Know About Monetizing Healthcare Data

Healthcare services providers generate huge amounts of data in the course of any given year. Many organizations, though, see this work as a source of financial losses. In a more modern view of the situation, however, all this healthcare data maintenance can be seen as a potential way to decrease losses and to create profit centers. Let’s explore some of the ways data monetization can benefit a business in the healthcare industry.

Ethical and Legal Concerns with Data Monetization

HIPAA is, rightly, the dominant issue when dealing with the legality of any monetization effort, but not as much as one might think. Bear in mind that anonymization, when performed competently, does cover the confidentiality issues related to HIPAA.

The more concerning problem is on the ethical side of the equation. In particular, efforts to anonymize data need to focus on ensuring identifying factors, such as addresses, Social Security numbers and even uniquely assigned identifiers aren’t traceable to any one patient. This can be surprisingly challenging, as evidenced by work from MIT researchers that found anonymized datasets could be mapped to individuals based on location data and networks.

When setting up data sets, you definitely want to discuss these worries in detail with the parties handling them. Other stakeholders, including doctors, patients and your organization’s lawyers should be included in the process.

One solution worth considering is asking patients to opt in to information sharing. This requires creating a framework that guarantees the confidentiality of the data, and there also needs to be legal language that explains patients’ rights in detail. Such documents should always include an opt-in process that requires a patient to clearly indicate their interest and to provide their consent. This is absolutely essential if you’re going to be monetizing data by selling to third parties.

Reducing Losses

Much of the low-hanging fruit in the industry comes from situations where data analysis can provide insights regarding losses. In the simplest form, this comes from streamlining processes, such as:

  • Scheduling appointments between doctors and patients more efficiently
  • Avoiding duplication of medical efforts
  • Preventing potential slip-ups
  • Maintaining contact with patients about screenings and check-ups

There’s also another level at which healthcare data can be utilized to spur the well being of patients. For example, insurance carriers and hospitals mining patient data have discovered trends among their customers where preventive measures can be taken. By providing those customers with medical devices, preventative screenings and other forms of care, they’ve reduced costs by avoiding more expensive, radical and reactionary solutions.

Healthcare data can also be utilized to establish the efficacy of medical options. Rather than relying on old habits and tried-and-true solutions, professionals can utilize large-scale to develop insights about which drugs produce the best outcomes for the dollar. Data is also employed in researching:

  • Genomic and epigenetic concerns
  • Pharmacology studies
  • New drug discoveries and treatments

Developing Healthcare Data Profit Centers

While HIPAA rules limit the amount of specific data that can be sold in relation to patient care, anonymized data is still a strong potential profit center. Researchers, insurance companies, government agencies and marketers are all looking for information that can fuel their own data analysis. This sort of data can benefit groups that are trying to develop:

  • Economic models
  • Government policies
  • Metastudies
  • Information regarding rare disease
  • Trend analysis

Packaging data for third parties carries with it several concerns that need to be taken seriously. Foremost, it’s important that all patient data be scrubbed of any identifying features. Secondly, large banks of data become targets for hackers, and it’s important to secure all your systems. Thirdly, aggregation of anonymous data will likely demand some investment in bringing in qualified data scientists, establishing in-house standards and building out computing resources.

There is also the cultural component that comes with all efforts to become more data-centric. Stakeholders need to be brought in on monetization efforts, and it’s critical to confirm they are on board with the technical, cultural, legal and ethical requirements of the process. While you don’t want to clear out folks who have honest objections, there usually are situations where stakeholders have to be bought out of contracts or given severance packages. Your goal during a monetization push should be to develop a larger organizational commitment to doing it well.

A commitment to data and monetization takes time. Resources and talent have to be accumulated, and data often has to be prepped for outside consumption. This means taking into account data consumers’ concerns about data lineage, unique identifiers and other information that allows them to do their job well. Being able to present both internal stakeholders and third parties with finished products can make offerings significantly more appealing.

Plenty of thought goes into monetizing data from a healthcare organization. In time, though, a portion of your business that seems like it costs you money can end up curtailing losses and generating new sources of revenue.

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Big Data Data Analytics Data Quality

What is Data Lineage & Why is it Important?

In the world of data analytics in 2019, keeping tabs on where bits of information came from, how they were processed and where they ended up at is more important than ever. This concept is boiled down to two words: data lineage. Just as a dog breeder would want to the lineage of a pooch they’re paying for, folks in the business intelligence sector want to know the lineage of the data that shows up in a final work product. Let’s look at the what, the why and the how of this process.

What is Data Lineage?

The simplest form of lineage for data is indexing items with unique keys that follow them everywhere. From the moment a piece of data is entered into a system, it should be tagged with a unique identifier that will follow it through every process it’s subjected to. This will ensure that all data points can be tracked across departments, systems and even data centers.

The concept can be extended significantly. Meta-data about entries can include information regarding:

  • Original publication dates
  • Names of authors
  • Copyright attributions
  • The date of the original entry
  • Any subsequently dates when it was accessed or modified
  • Parties that accessed or modified the data
  • Analytics methods that were used to process the data

In other words, the lineage functions as a pedigree that allows anyone looking at it to evaluate where it came from and how it got where it is today.

Why Does This Matter?

Within the context of business intelligence, there will always be questions about the inputs that went into a final product. Individual data points can be reviewed to discover problems with processes or to show how transformations occurred. This allows folks to:

  • Perform quality control on both the data and analytics techniques
  • Explain how particular insights were arrived at
  • Consider alternative approaches
  • Refine techniques
  • Mine older sources of data using new technologies

When someone wants to pull a specific anecdote from the data, the lineage allows them to get very granular, too. In the NBA of 2019, for example, shot location data is used to study players, set defenses and even choose when and where to shoot. If a coach wants to cite an example, they can look through the lineage for a shot in order to find film to pull up.

The same logic applies in many business use cases. An insurance company may be trying to find ways to deal with specific kinds of claims. No amount of data in the world is going to have the narrative power of a particular anecdote. In presenting insights, data scientists can enhance their presentations by honing in on a handful of data points that really highlight the ideas they’re trying to convey. This might include:

  • Providing quotes from adjuster’s reports
  • Comparing specifics of an incident to more generalized insights
  • Showing how the numbers align
  • Talking about what still needs to be studied

Data governance is also becoming a bigger deal with each passing year. Questions about privacy and anonymization can be answered based on the lineage of a company’s data. Knowing what the entire life cycle of a piece of information is ultimately enhances trust both within an organization and with the larger public.

Cost savings may be discovered along the way, too. Verification can be sped up by having a good lineage already available. Errors like duplication are more likely to be discovered and to be found sooner, ultimately improving both the quality and speed of a process. If a data set is outdated, it will be more evident based on its lineage.

The How

Talking about data lineage in the abstract is one thing. Implementing sensible and practical policies is another.

Just as data analytics demands a number of particular cultural changes within an organization, caring about lineage takes that one step further. It entails being able to:

  • Document where all the company’s data came from
  • Account for who has used it and how
  • Explain why certain use cases were explored
  • Vouch for the governance of the data with a high level of confidence

At a technical level, databases have to be configured to make tracking lineage possible. Data architecture takes on new meaning under these circumstances, and systems have to be designed from the start with lineage in mind. This can often be a major undertaking when confronting banks of older data. If it’s implemented in the acquisition and use of new data, though, it can save a ton of headaches.


Tracking the lineage of a company’s data allows it to handle a wide array of tasks more professionally and competently. This is especially the case when pulling data from outsides sources, particularly when paying for third-party data. Not only is caring about lineage the right thing to do, but it also has a strong business case to back it up.


Big Data Data Science Careers

8 Tips & Tricks for Data Scientists

Whether you already work in the data science field or wish to get into it, there’s a lot of benefit in always expanding your bag of tricks. The field is grounded in statistics, and there’s also a rapidly growing trend toward automation. Being tech- and math-savvy is absolutely critical. Let’s take a look at 8 tips and tricks you’ll want to know as a data scientist.

#1: Learn to Program

With data science already heavily dependent on computing resources and machine learning quickly become the top way to derive insights, coding skills have never been more important. Fortunately, you don’t have to be a full-fledged application developer. Several programming languages are being increasingly tailored to serve those who need to build their own data analysis tools. Two of the biggest languages worth keeping up with are:

  • Python
  • R

If you’re looking to perform work using modern machine learning systems like TensorFlow, you’ll likely want to steer toward Python, as it has the largest set of supported libraries for ML. R, however, is very handy for quickly mocking up models and processing data. It’s also prudent to pick up some understanding of database queries.

#2: Develop a Rigid Workflow for Each Project

One of the biggest challenges in the world of data analytics is keeping your data as clean as possible. The best way to meet this challenge head on is to have a rigid workflow in place. Most folks in the field have set down these steps to follow:

  1. Gather and store data
  2. Verify integrity
  3. Clean the data and format it for processing
  4. Explore it briefly to get a sense of the dataset’s apparent strengths and weaknesses
  5. Run analysis
  6. Verify integrity again
  7. Confirm statistical relevance
  8. Build end products, such as visualizations and reports

#3: Find a Focus

The expanding nature of the data analytics world makes trying to know and explore it all as impossible as getting to the edge of the universe. It might be fun to explore machine vision to identify human faces, for example, but that skill likely isn’t going translate well if your life’s work is doing plagiarism detection.

In order to find a focus, you need to look at the real-world problems that interest you. This will then allow you to check out the data analysis tools that are commonly used to solve those problems.

#4: Always Think About Design

How you choose to analyze data will have a lot of bearing on how a project turns out. From a design standpoint, this means confronting questions like:

  • What metrics will be used?
  • Is this model appropriate for this job?
  • Can the compute time be optimized more?
  • Are the right formats being used for input and output?

#5: Make Data Scientist Friends with Github

Github is a wonderful source of code, and it can help you avoid needlessly reinventing the wheel. Register an account, and then learn the culture of Github and source code sharing. That means making a point of providing attribution in your work. Likewise, try to contribute to the community rather than just taking from it.

#6: Curate Data Well

One of the absolute keys to getting the most mileage out of data is to curate it competently. This means maintaining copies of original sources in order to allow others to track down issues later. You also need to provide and preserve unique identifiers for all your entries to permit tracking of data across database tables. This will ensure that you can distinguish duplicates from mere doppelgängers. When someone asks you to answer questions about oddities in the data or insights, you’ll be glad you left yourself a trail of breadcrumbs to follow.

#7: Know When to Cut Losses

Digging into a project can be fun, and there’s a lot to be said for grit and work ethic when confronting a problem. Spending forever fine-tuning a model that isn’t working, though, carries the risk of wasting a significant portion of the time you have available. Sometimes, the most you can learn from a particular approach is that it doesn’t work.

#8: Learn How to Delegate

Most great discoveries and innovations in the modern world are the final work products of teams. For example, STEM-related Nobel Prize are pretty much never awarded to individual winners anymore. While the media may enjoy telling the stories of single founders of companies, the reality is that all the successful startups of the internet age were team projects.

If you don’t have a team, find one. Recruit them in-house or go on the web and find people of similar interests. Don’t be afraid to use novel methods to find team members, too, such as holding contests or putting puzzles on websites.

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Big Data Data Analytics

Your Company Needs to Be Data Driven, Here’s Why

Data driven decision making is an increasingly important part of the modern business landscape. Amazingly enough, 58% of business leaders who responded to one survey said that the majority of their decisions are still based on gut feelings or experience rather than data. While the human element will never be eliminated from the process of making decisions, there’s a strong argument for an organization focusing on developing a data driven attitude.

Data Driven Decision Making Frequently Fixes Biases

For most industries, making money is a question of discovering what hasn’t yet been exploited by other companies. Spotting and exploiting these sorts of inefficiencies allows firms to gain first-mover advantages.

The folks who run call centers at Xerox Services turned to big data to reassess how they pick job candidates for interviews. The initial proposed solution based on the data left some managers downright shocked. In some cases, the system was actually sending in people with no relevant prior experience. It also singled out individuals who were on four or more social networks to not be sent in. As the program moved forward, though, attrition rates for new hires dropped 20%.

How did this happen? Data driven decision making often moves companies past human biases. Human hiring managers frequently look for signals that feel relevant but aren’t. The machines cut out all the noise of human interaction, focusing on results rather than imputing biases.

The Data Analytics Arms Race

In some industries, building out data analytics capabilities is well on its way to being an arms race between companies. The NBA has been revolutionized by analytics, with the league utilizing technologies derived from missile-tracking systems to keep tabs on every footstep and dribble made in each game. A league that was once dominated by the slam dunk rapidly switched to 3-pt shooting, and the Golden State Warriors are widely considered the first champion built on hard data. Other teams have since been racing to catch up.

On Wall Street, companies that use programmatic trading and algorithms are considered dinosaurs stuck in the 1980s. Private equity has long since moved beyond learning from the past and is now focused on predictive data analytics. One high-frequency trading firm posted a profit in 1237 out of 1238 trading days. It’s easy to see why “data scientist” is the hottest job trend in finance.

Data Driven Marketing

Some sectors have found the concurrent rise of social media and big data to be the confluence of events they required to get out in front of the competition. For large corporations, this has allowed them to target niches that were often unreachable. If you’ve walked through the grocery store and read the packages, there’s a good chance you’ve seen data driven marketing in action. Brands like Betty Crocker and General Mills now frequently emphasize niche selling points such as “non-GMO” and “gluten-free.” These selling propositions were designed by sifting through social media data to find what concerns drove consumer decisions. The brands then adjusted their marketing to have appeal to both the general public and niche markets, allowing them to maximize their exposure without making massive investments in advertising. Instead, they changed a few things on their packages.

Cutting Costs

The difference between a profitable year and a bad one often boils down to nothing more than costs. Nearly 50% of Fortune 1000 firms say they’ve started data driven initiatives to cut expenses and seen a return on the investment.

In the fashion world, using big data to track trends has become a key part of the purchasing process. No one wants to be sitting on inventory because they made the wrong buy or bought at the wrong moment. Timing this out can be challenging, too, as most retailers depend on global supply chains to bring purchased inventory from overseas to target markets. By monitoring social media trends, for example, a fashion retailer can send real-time data to a buyer in Bangladesh informing them of what styles are trending and how strongly. That can be distilled to data that enables a buyer on the other side of the planet to determine everything from purchasing volume to shipping method.

Becoming a Data Driven Operation

It’s not enough to want your company become a data driven organization. You need to lay out a plan that gets you there. This includes:

  • Fostering a culture that values data
  • Putting standards in place
  • Hiring professionals with big data skills
  • Educating stakeholders about the advantage of driving decisions with data
  • Building out the necessary infrastructure, particularly computer servers
  • Adjusting hiring practices to incorporate big data skills
  • Opening up the discussion to all parties from top to bottom

The move to a data-centric worldview also means getting tough about things. Companies often end up using severance packages to ship out folks who refuse to get on board with the changes. This requires a hard look at why certain people are employed and whether they can adjust to the new reality.

Ultimately, a data driven approach is about competitiveness. Other companies are already doing it and succeeding. The sooner your operation becomes one that values data, the sooner it can attract the right candidates for jobs and become more competitive.

Big Data Data Analytics

Data Security is Crucial to Business Prosperity

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.


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

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:

  1. 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)
  2. 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)
  3. 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

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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