Author: Scottie Todd

Digital Marketing Lead

3 Things Companies Are Doing Right Now to Bounce Back

Post author By Scottie Todd
Post date February 22, 2021

The past year has been filled with a long list of unforeseen challenges for all organizations. This uncertainty has left businesses with more and more questions that need to be answered, particularly when it comes to their strategies moving forward.

There’s no question that data and analytics are paving the way for businesses in the post-pandemic world, but what exactly are companies doing right now to bounce back? Let’s take a closer look at what leading companies are doing to get back on track and prepare for growth in a post-COVID world.

Investing in the Digital Future

There has always been an enormous amount of talk surrounding the need for digital transformation. Organizations have long used the buzzword loosely when discussing their strategy and high-level goals. For many years, there has been a recognized need for these digital efforts but the transformation has been slow to move. The recent market disruptions, though, have catalyzed digital transformation and emphasized the importance of cost optimization and process improvement through new digital strategies.

Industry leaders are doubling down on their investment in digital strategy and IT implementation. According to a recent Gartner survey, 69% of board of directors have accelerated their digital business initiatives due to COVID’s disruption. This has caused an increase in “forward-looking investments” that will aid in quick responses to unexpected events. Through widespread digital transformation, there has been an apparent shift towards preparedness and the improved agility of organizations as a whole.

Additionally, digital transformation opens doors for things like customer engagement due to increased customer visibility and opportunities for personalization. Many companies are entirely transforming their business model while expanding their digital product offerings as a means for revenue growth.

To start accessing your own digital strategy, start by asking questions such as:

How is your digital investment aligned with your business goals?
What metrics and KPIs need to be tracked to effectively measure change?
What strategies and high-level goals should be understood at all levels of the organization?

Data-Driven Decision Making

Decisions, decisions, decisions. The one thing you can always count on to remain constant in business, regardless of any change and uncertainty in the business environment. The road following a disruption, though, is filled with new questions surrounding customer behaviors and strategic decisions that need to be made quickly.

The recent marketplace changes have highlighted the need for rapid decision-making. Rapid decision-making falls into both an organization’s preparedness and ability to adapt to evolving situations. Using data to drive and inform decision-making is no longer considered a competitive advantage, but instead is identified as a need in order to compete. Whether you’re predicting staff and inventory requirements or evaluating online buying behaviors through customer analytics, data should be at the core.

Not Putting Things Off

Arguably the most important thing companies are doing to bounce back from the downturn is taking action. The time is now, stop putting things off! The vast majority of companies deferred a number of initiatives and business development efforts due to COVID. You’ve likely heard it used as an excuse to delay or revisit a project in the future yourself.

Nevertheless, progress or effective change never came from putting something on the back burner. You can’t expect growth to happen on its own and continue to delay efforts until things go back to what was once considered normal. Reevaluate your priorities, preparedness, and strategies from the individual level all the way up to the overarching organization.

Review

To sum up, the points stated above, the key to moving forward is all about adapting to changing situations. Whether it’s your ability to quickly generate insights that will drive decision-making or investing in new digital channels, it all comes back to how prepared you are to respond and adapt to change.

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Big Data Data Analytics

7 Bad Habits Every Data Scientist Should Avoid

Post author By Scottie Todd
Post date February 15, 2021

Are you making these mistakes? As a data scientist, it can be easy to fall into some common traps. Let’s take a look at the most common bad habits amongst data scientists and some solutions on how to avoid them.

1. Not Understanding the Problem

Ironically, for many data scientists, understanding the problem at hand is the problem itself. The confusion here often occurs for a couple of reasons. Either there is a disconnect between the data scientist’s perspective and the business context of the situation or the instructions given are very vague and ambiguous. These reasons all lead back to a lack of information and understanding of the situation.

Misunderstandings of the business case can lead to wasted time spent working towards the wrong approach and often causes many unnecessary headaches. Don’t be afraid to ask clarifying questions, having a clear picture of the business problem being asked is vital to your efficiency and effectiveness as a data scientist.

2. Not Getting to Know Your Data

We’re all guilty of wanting to jump right in and get the ball rolling, especially when it comes to a shiny new project. This ties into the last behavioral point, rushing to model your data without fully understanding its contents can create numerous problems in itself. A thorough and precise exploration of the data prior to analysis can help determine the best approach to solving the overarching problem. As tempting as it may be, it’s important to walk before you can run.

After all, whatever happened to taking things slow? Allocate time for yourself early on to conduct an initial deep dive. Don’t skip over the getting to know you phase and jump right into bed with the first model you see fit. It might seem counterintuitive but taking time to get to know your data at the beginning can help save time and increase your efficiency later down the line.

3. Overcomplicating Your Model

Undoubtedly, you will face numerous challenges as a data scientist, but you will quickly learn that a fancy and complicated model is not a one size fits all solution. It’s common for a complex model to be a data scientists’ first choice when diving into a new project. The bad habit, in this case, is starting with the most complex model when a more simple solution is available.

Try starting with the most basic approach to a problem and expand your model from there. Don’t overcomplicate things, you could be causing yourself an additional headache with the time drained into the more intricate solution.

4. Going Straight for the Black Box Model

What’s worse than diving in headfirst with an overly complex model? Diving in headfirst with a complex model you don’t entirely understand.

Typically, a black box is what a data scientist uses to deliver outputs or deliverables without any knowledge of how the algorithm or model actually works. This happens more often than one might think. Though this may be able to produce effective deliverables, it can also lead to increased risk and additional problems. Therefore, you should always be able to answer the question of “what’s in the box?”

5. Always Going Where No One Has Gone Before

Unlike the famous Star Trek line, you don’t always have to boldly go where no man has gone before in the realm of data science. While being explorative and naturally curious when it comes to the data is key to your success, you will save a lot of time and energy in some cases by working off of what’s already been done.

Not every model or hypothesis has to be a groundbreaking, one of a kind idea. Work from methods and models that other leaders have seen success with. Chances are that the business questions you’re asking your data or the model you’re attempting to build have been done before.

Try reading case studies or blog posts speaking on the implementation of specific data science projects. Becoming familiar with established methods can also give you inspiration for an entirely new approach or lead you to ideas surrounding process improvement.

6. Doing It All Yourself

It’s easy to get caught up in your own world of projects and responsibilities. It’s important, though, to make the most of the resources available to you. This includes your team and others at your organization. Even your professional network is at your disposal when it comes to collecting feedback and gaining different perspectives.

If you find yourself stuck on a particular problem, don’t hesitate to involve key stakeholders or those around you. You could be missing out on additional information that will help you to better address the business question at hand. You’re part of a team for a reason, don’t always try to go it alone!

7. Not Explaining Your Methods

The back end of data science projects might be completely foreign to the executive you’re working within marketing or sales. However, this doesn’t mean you should just brush over your assumptions and process to these non-technical stakeholders. You need to be able to explain how you got from point A to point B, how you built your model, and how you ultimately produced your final insights in a way that anyone can understand.

Communication is essential to ensure the business value is understood and properly addressed from a technical standpoint. Though it might be difficult to break things down in a way that non-technical stakeholders can understand, it’s important to the overall success of any project you will work on. This is where storytelling tactics and visualizations can come in handy and easily allow you to communicate your methods.

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Big Data Business Intelligence Data Science Careers

How to Become the BI Rookie of the Year

Post author By Scottie Todd
Post date February 8, 2021

With the new year upon us and new opportunities at hand, it’s time to get your head in the game. There’s a business intelligence all-star in us all. Whether you’re new to the business intelligence league, have recently been transferred to a new team, or are just looking to up your data game, here are some strategies that can help.

Practice Like You Play

While practice might not always make perfect, it helps you get a little closer in your continuous pursuit. Natural talent can only take you so far, making practice essential to developing skills and gaining experience. Try taking on an additional practice project dedicated to advancing your technical skills. This will ensure your improvement over time and allow you to experiment outside of the common workplace parameters of traditional methods and time constraints.

You should begin by picking out your “play data.” However, it’s important to note that this shouldn’t be just any old data. Find data related to something that interests you, take inspiration from any personal hobbies you may have. This could range from anything such as your music listening activity, your local weather data, or even your fantasy football league.

Practicing your skills with data you have a personal invested interest in will give you the opportunity to play and experiment with new techniques. This will also increase your chances of continuing these development efforts. These projects are also a great way to demonstrate your curiosity when it comes to data, which can be key when looking to get drafted by another organization.

If you’re stuck on sparking an initial idea, try researching open-source data and see if anything catches your eye. Resources like the Registry of Open Data on AWS or the U.S. government’s open data library are great places to start.

You can also see Inzata’s guide on Where to Get Free Public Datasets for Data Analytics Experimentation.

Learn From the Pros

Regardless of how much time you’ve spent in the league so far, there will always be more to learn from those around you. Taking the student approach or possessing the rookie mindset is vital to continuous learning.

Start by looking to those who have demonstrated success in their field. This can be anyone from the higher-ups within your organization to an industry influencer. You can develop these connections by asking an executive to lunch or joining various discussion forums and networking groups.

Additionally, try attending as many training’s, webinars, and conferences as you can. These virtual events are more accessible than ever due to the recent widespread transition to remote work, giving you access to thought leaders across the globe.

There is also an abundance of available content online such as books and online courses. These resources will give you instant access to decades of industry experience and valuable lessons learned through hands-on accounts.

What’s Your Next Home Run?

Your batting average when it comes to tasks and projects is crucial to long term success. You might get lucky every once in a while if you’re aimlessly swinging for the fences. It’s important, though, to make sure you are establishing attainable goals for yourself. You can think of these goals as the next home run you’re looking to hit. Having a clear vision of professional milestones will help guide you in the right direction and ultimately increase your chances of achievement.

Studies show that you’re 42% more likely to achieve your goals if you simply write them down.

One strategy that will improve your batting average is to determine SMART goals for yourself in terms of your role and what you’d like to achieve. Now, these goals aren’t just smart in the traditional sense of the word.

SMART is an acronym for:

Specific – Make your goals clear and concise. Don’t leave any room for ambiguity or confusion, these goals should be as specific as possible.
Measurable – Evaluation is essential. You need to be able to measure your advancement towards your goal through metrics or other methods. How will you measure your progress? What metrics or evidence will you track in order to assess your efforts?
Achievable – Make sure you aren’t setting goals outside of your reach. While these goals should be challenging, it’s important to remain within the realm of attainability.
Relevant – Your goals should be tied to the broader goals of your organization and what your department is trying to achieve.
Time-Based – Setting a timeline will help you manage your time and implement a sense of urgency into your efforts.

This strategy is effective in that it sets up clear and measurable targets, increasing your chances of knocking a project or metric out of the park.

Your goals should be challenging enough so that you won’t be able to hit one every game and it’s by no means comparable to winning your organization’s world series, but it’s a small win helping to mark your development as a player.

Around the Bases

Overall, this post demonstrates the many tactics and resources available at anyone’s disposal to immediately up your data game. Finding the BI all-star in you ultimately comes down to how you’re investing in yourself. Improvement is a slow and steady process, make the most of the knowledge around you and experiment with what interests you. Implement these tips and strategies to start your journey to the hall of fame!

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Data Science Careers

5 In-Demand Traits of Highly Effective Data Scientists

Post author By Scottie Todd
Post date February 1, 2021

Demand has consistently been on the rise for data science roles and analytics skills across all industries. In 2021, it’s predicted that there will be an additional 3,037,809 new job openings in data science worldwide as companies move to become data-driven.

Whether you’re an aspiring data scientist yourself or just looking to acquire the mindset of one, knowing the essential qualities it takes to succeed in the role can help highlight what to focus on in your development. This leads us to the question: What does it take to be a successful data scientist? Here are the traits that set effective data scientists apart from the rest.

Business Vision

The most successful data scientists commonly have the ability to understand the company’s situation from a business standpoint and always keep the organization’s overarching goals in mind. This is important to understand the why behind the data. Business acumen is the key to determining what critical business questions the data is looking to answer or what questions need to be asked.

Being a data scientist isn’t solely about writing code and developing data models. General knowledge of organizational goals and challenges can help you to start asking the right questions and develop useful queries.

Analytical Reasoning

Due to the technical nature of the position, possessing analytical and critical thinking skills is necessary for success. Working with data is all about identifying patterns and thinking quantitatively. Data scientists need to be able to look at any particular problem objectively in order to come to logical conclusions.

Scientists should also be considering different angles and perspectives when looking at their data. Constantly asking questions and deriving insights from various points of view are crucial to driving effective and objective analysis.

Curiosity

A data scientist’s abilities should extend far beyond their technical expertise. Soft skills such as curiosity and creativity are what distinguish good scientists from great ones. The job title contains the word ‘scientist’ for a reason, not all of the answers are known! You will need to theorize, develop hypotheses, experiment, and ultimately draw conclusions on a day to day basis.

Curiosity is needed when it comes to handling these tasks and diving deeper into the complex problems at hand. Great scientists should take an iterative approach to understand their data and be open to questioning their initial assumptions. Highly effective data scientists are always looking for the “why” and “how” behind the data in order to probe for additional information. Exploration and experimentation are vital to producing conclusive insights.

Communication

Furthermore, another essential skill for data scientists is communication. One of the primary responsibilities of a data scientist is to communicate their methods and findings to other business units. With the abundance of data today, it can be messy and difficult to understand, especially if you are entirely unfamiliar with the data.

It’s important to communicate insights in a way that’s easy for others to understand and drive decisions from. After all, what good is the data if no one can understand it? Any skills surrounding storytelling abilities or communication will help to properly inform key stakeholders on their queries.

Collaboration

Whether you are working within your department or with others to collect and communicate data, collaboration is critical. Much like every job, teamwork plays a vital role in maximizing productivity. This working relationship, though, is especially important for data scientists as there is often a disconnect between business units and data science teams. Data scientists help to bridge the gap between the two business functions and allow for greater effectiveness all around.

Conclusion

Overall, many traits can contribute to the success of a data scientist. But it’s important to note that none of these traits are necessarily required or set in stone when it comes to the makings of an effective one. The role contains a number of unique responsibilities but there remains an opportunity to make it entirely your own. Consider these traits as a common data scientist’s keys or guide to data mastery when developing in their career.

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BI Best Practices Business Intelligence Data Analytics

How to Transform Your Lazy BI Team

Post author By Scottie Todd
Post date January 25, 2021

Are lazy BI practices getting the best of your team? It’s not uncommon for teams to become entrenched in their usual way of doing things, particularly when repeatable and seemingly mundane tasks are involved. There is always an opportunity for growth and process improvement in any team, but a case of lazy BI can make any new methods or change difficult to implement.

If you’re looking to ignite change, don’t worry, best practices are always here to lend a hand with everything from data sources to server management. Whether you’re dealing with the self-taught BI wizard of the team or just a tired coworker, here are some strategies that can help.

If You’re New to the Team

Before we jump into strategies, if you’re in the unique situation of being new to the team, there are a few things you should keep in mind. Though it might be easier for you to see where improvements need to be made as an outside source, it’s important to establish rapport with your teammates before rushing to make changes.

Begin by observing and make note of potential adjustments to workflows or processes. Additionally, be inquisitive and ask questions to figure out the why behind methods that aren’t considered to be best practice. After you’ve allowed some time to get a feel for the entirety of the situation, consider these methods when developing your approach.

Why Change if Nothing’s Broken?

Why should you do things differently if your current methods are getting the job done? Don’t be surprised if you receive the ‘if it ain’t broke, don’t fix it’ mentality in response. This is a common and natural resistance to changes proposed, the way you go about bringing people on board is essential.

Use the Laziness to Your Advantage

It’s not uncommon for new processes or best practices to be swept under the rug following their initial introduction. While new ways of doing things might be more efficient and a good idea on paper, no change can survive without successful adoption from the majority.

The key is appealing to less work and effort exerted in the future. Even though it might take time to adjust and create increased work for your team initially, it’s important to emphasize the mass amounts of time they will save in the future.

In this approach, you’ll be responding to the age-old question of “what’s in it for me?”

Though best practices are better for productivity and the organization as a whole, how will these changes directly benefit those involved? Appealing to the desire at an individual level will increase your chances of successful implementation.

Start Small

The key to any kind of change is to start small. Upheaving the old methods to make way for new ones is extremely disruptive and can be overwhelming to most. Starting small will increase your chances of a successful adoption.

Find something small your team can achieve or begin to change. This is the same philosophy people use when altering their life by doing something as simple as making your bed each day. Though it is a small task, it helps those involved to feel accomplished. This makes one more likely to be productive elsewhere in their day as well as being open to greater change.

Conclusion

Overall, it’s important to remember that there is no ironclad rule or gold standard for the successful adoption of new methods. There is no absolute anecdote or cure to a case of lazy BI. Regardless of which strategies or tactics you’re using to influence change, every team is different in the way they learn and adapt. Each scenario bears its own unique characteristics in terms of behavior, environment, and the topic of change itself. As a leader and a teammate, it’s up to you to access these factors and strategize your approach accordingly.

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Artificial Intelligence Big Data Business Intelligence Data Analytics

Press Release: Inzata Analytics: Expanding its Reach to the DoD with Nobletech Solutions

Post author By Scottie Todd
Post date January 18, 2021

Inzata Analytics, a data analytics software company, has announced its partnership with Nobletech Solutions Inc., a provider of technology and engineering services. This partnership will address the DoD’s full spectrum of data challenges ranging from prognostic & predictive maintenance (PPMx), logistics, and sustainment to intelligence, human resources, and fiscal management without the complexity and delays that existing DoD data analytics tools provide.

Inzata provides the software to bring data analytics to end-users within hours and days at all levels within DoD organizations, regardless of user-level experience. Nobletech provides the proper secure network, DoD experience, and contract vehicles that will put Inzata’s AI and data analytics solutions into the hands of those front-line users when the data is needed to make those critical decisions.

Inzata’s ability to assemble large disparate data sources without coding enables data analysis to occur in significantly shorter times than any of its competitors. The current political climate cannot afford to wait months and years for analysts and data architects to develop products for decision makers while Inzata’s AI/ML and no-code solution can have it done in days and weeks, vs. months and years that the competition requires.

Nobletech Solutions will bring Inzata into the DoD by leveraging their GSA Schedule while pursuing a DoD Enterprise Software Agreement (ESA). This will be done by demonstrating to the DoD and its industry partners the power of Inzata’s Artificial Intelligence/Machine Learning (AI/ML) capability to perform rapid assembly and analysis of data and visual dashboards through its unique no-code solutions.

It is certain that Nobletech Solutions with the ability to host on the DoD secret cloud, without the required add-ins and extras required by those other data analytics platforms, will surely be a significant mission enabler to special operations and other organizations within the DoD and OGA.

Nobletech Sr. Analyst: “Providing an edge to the DoD to quickly assemble large data with the help of AI/ML will certainly be a game changer. We envision Inzata as the tool to provide that big picture view of people, equipment, parts, location, training, intelligence, risk, environmental conditions, and other data needed by the most senior level commanders down to the small unit leader.”

Nobletech CEO: “We are excited to have this partnership and look forward to being a key player in meeting the needs of DoD’s data analytics pertaining to human resource, material, training/qualification, prognostic maintenance, and intelligence.”

For more information, please contact the following:
Jim Scala at jscala@nobletechsolutions.com
Luke Whittington at lwhittington@nobletechsolutions.com
You can also learn more by visiting https://www.nobletechsolutions.com/data-analytics

This press release was originally featured on PRWeb, find the press release here: http://www.prweb.com/releases/inzata_analytics_expanding_its_reach_to_the_dod_with_nobletech_solutions

Big Data Data Analytics Data Preparation

The Beginner’s Guide to Data Streaming

Post author By Scottie Todd
Post date January 18, 2021

What is Data Streaming?

Data streaming is when small bits of data are sent consistently, typically through multiple channels. Over time, the amount of data sent often amounts to terabytes and would be too overwhelming for manual evaluation. While everything can be digitally sent in real-time, it’s up to the software using it to filter what’s displayed.

Data streaming is often utilized as an alternative to a periodic, batch data dump approach. Instead of grabbing data at set intervals, streamed data is received nearly as soon as it’s generated. Although the buzzword is often associated with watching videos online, that is only one of many possible implementations of the technology.

How is Data Streaming Used?

Keep in mind that any form of data may be streamed. This makes the possibilities involving data streaming effectively limitless. It’s proven to be a game-changer for Business Analytics systems and more. From agriculture to the fin-tech sector to gaming, it’s used all over the web.

One common industry application of data streaming is in the transportation and logistics field. Using this technology, managers can see live supply chain statistics. In combination with artificial intelligence, potential roadblocks can be detected after analysis of streamed data and alternative approaches can be taken so deadlines are always met.

Data streaming doesn’t only benefit employees working in the field. Using Business Analytics tools, administrators, and executives can easily see real-time data or analyze data from specific time periods.

Why Should We Use Data Streaming?

Data silos and disparate data sources have plagued the industry for countless years. Data streaming allows real-time, relevant information to be displayed to those who need access to it the most. Rather than keeping an excessive amount of data tucked away on a server rarely accessed, this technology puts decision-driving information at the forefront.

Previously, this type of real-time view of business processes was seen as impossible. Now that it’s possible to have an internet connection almost everywhere, cloud computing makes live data streaming affordable, and Business Analytics tools are ready to implement data streaming, there’s no reason it would be inaccessible.

While it may be tempting to stick to older ways of processing data, companies who don’t adapt to this new standard will likely find it more difficult to remain competitive over the years. Companies that do incorporate the technology will likely see their operations become more streamlined and find it much easier to analyze and adjust formerly inefficient processes.

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Data Analytics Data Science Careers

Data Scientist vs. Data Analyst: What’s the Difference?

Post author By Scottie Todd
Post date January 4, 2021

In today’s business climate, data is the one thing everyone is looking to as a means to compete and drive better decision making across business units. But who in your organization is actually working with data and putting it to work? You’ve likely seen an abundance of job listings for data analysts and data scientists alike or may even currently be in one of these roles yourself. These positions are becoming increasingly essential across industries, the Harvard Business Review even deemed data scientist as the “sexiest job” of the 21st century.

However, the lines can often be blurred between the roles of data scientists and data analysts. So now that we’ve established the rising demand and importance of these common positions, let’s take a closer look at understanding each. Let’s explore what it means to be a data scientist or an analyst as well as some key distinctions to keep in mind.

What do Data Analysts do?

Data analysts are versatile in their role and are predominantly focused on driving business decisions. It’s common for data analysts to start with specific business questions that need to be answered.

Some common job functions for data analysts include:

Identify trends and analyze data to develop insights
Design business intelligence dashboards
Create reports based on their findings
Communicate insights to business stakeholders

What do Data Scientists do?

While a data scientist also works with data thoroughly to develop insights and communicate them to stakeholders, they commonly apply more technical aspects to their work. This includes things such as coding, building algorithms, and developing experiments. Data scientists must know how to collect, clean, and handle data throughout the pipeline.

Data scientists typically require more experience and advanced qualifications, specifically when it comes to their knowledge of statistical computer languages such as Python and SQL. However, there is far more to a data scientist’s role than merely their technical expertise. They have to be able to ask the right questions and streamline all aspects of the data pipeline.

Some common tasks and responsibilities for data scientists include:

Build and manipulate data models
Optimize data collection from disparate data sources
Clean data and create processes to maintain data quality
Develop algorithms and predictive models

What’s the Difference?

While both roles have data in common, the primary difference between the two is how they work with data. Data scientists focus on the entire data lifecycle, from collection and cleaning to final insights and interpretation. A data analysts’ role is weighted at the end of the pipeline, this being the interpretation of data and communicating findings to business units.

It’s not uncommon for data analysts to transition into the role of data scientist later on in their careers. Many view data analysts as a stepping stone, where analysts are able to practice the foundational tools of working with data.

How Much do Data Scientists and Data Analysts Make?

Your second thought after “show me the data” is probably “show me the money!” Now that we’ve reviewed the similarities and differences in each role’s responsibilities, let’s get down to the numbers. According to Glassdoor, you can expect to earn an average base pay of around $113,309 as a Data Scientist. This is nearly double the average base pay for a Data Analyst which comes in at around $62,453 per year. The seemingly drastic pay difference primarily reflects the variation in technical expertise and years of experience needed.

Conclusion

Overall, there is no predetermined definition of what it means to be a data analyst or a data scientist. Each role is unique and will vary depending on the industry, there are also a number of other factors specific to each organization. Though, it’s important to remember that there is room to make the position your own and define either job title for yourself.

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Big Data Education

How Is Data Analysis Affecting the Education Sector in 2021?

Post author By Scottie Todd
Post date January 2, 2021

When people think about Big Data, most of the ideas that come to mind involve businesses or governments trying to deal with large-scale issues. Many use cases involving Bid Data and analytics boil down to questions that can be answered if you happen to have a massive number of data points to work with. This means that the education sector is, in fact, one of the ripest areas for new analytics work to be done.

Why the Education Sector?

In the simplest terms, education and analytics go together well because of the need to teach hundreds of millions of both children and adults each year. From preschools to doctoral programs, most people on the planet now receive at least some formal education.

This means that there is data about people across many cultures, and that has the potential to act as a filter in performing analysis. One of the biggest concerns in the world of Big Data is that datasets are imputing biases. Organizations sharing data at the global level have access to information that can be used to filter out biases, normalize what performance should be and devise better class plans based on hard data science.

Similarly, academia has a much better tradition of sharing than other areas where analytics has made major inroads, especially finances. While folks on Wall Street are often worried about keeping their findings tightly guarded, educators generally want to share their discoveries as far and wide as possible.

What Are the Potential Use Cases?

Intervention

Intervention is an important job at all ages in the education process. A classic case where Big Data has been in use longer than the term “Big Data” has been around is in tracking high school and college dropout rates. While plenty of data on the subject has been available for decades, the opportunity to apply analytics has made an old use case into a fresh one.

Detecting wobbliness in student performance, for example, can be a challenge for a human being to do. It’s difficult for a single teacher to spot a student whose grades are starting to slip. At a macro scale, though, potential dropouts can be identified from the larger pool by matching them to previous students who matched the pattern. Schools can then direct resources such as study aid, financial help and even counseling toward students who might be at risk.

Career Paths

Even the best and most stable performers can feel challenged by picking a career path. Students can be tracked using multiple datasets and questionnaires to determine what career paths match their interests and what courses they should be taking. If a student wants to get into a STEM field, for example, a model can be worked up that will guide them in their high school course selection. This can ensure that they’ll be better prepared when they get to college. Similarly, students who are a bit tepid can be directed toward courses that will help them find their paths.

Forecasting

While it’s important to note that analytics systems aren’t oracles, there’s something to be said for trying to forecast students’ grades based on demographics, curricula, institutions and other factors. If the forecast for an elementary school student looks worrisome, interventions can then be arranged to make up for gaps that might only appear years or even decades down the road.

Likewise, the same approach can be used to intervene at schools that may be on the brink of trouble. This can be especially helpful when looking at problems like budgeting, teacher allotments, deploying resources and even shutting down schools. A district may be able to run multitudes of simulations to drill down to what is the ideal composition for a district. The goal should ultimately be to make the best use of the dollars available for each student.

Teaching Styles

One of the hardest issues to address in education is getting teachers who may be laggards to perform as the best educators do. While it’s tempting to tag these folks as “bad” teachers, the reality is often that they don’t quite have the magic formula for controlling a classroom and engaging with students. It’s also easy to dismiss the best teachers as talented, but it is possible to track what they do well. With this template in hand, interventions can be done during their time in universities and during teacher training to adapt their skills to what works.

How to Become Data-Centric

Every organization that moves toward analytics has to embrace a new culture. On one end, this means embracing data and its use. In some cases, this even means letting go of administrators who can’t get on board with analytics. Conversely, it’s also important to make sure that happy adopters appreciate the importance of dealing competently with issues like data privacy, biases, anonymization, errors and the limitations of analytics-driven decision-making.

It will take time to build an educational system that used data to improve life for both educators and students. The field, however, is ripe with available data. Education is a sector that is fertile ground for analytics, and there is also an installed based of interested educators who can plow the ground. With direction and resources, virtually any school can benefit from adopting Big Data and analytics.

Big Data Business Intelligence Data Analytics

How to Effectively Leverage Data Wrapping

Post author By Scottie Todd
Post date December 17, 2020

What Is Data Wrapping?

Data wrapping is a strategy utilized by many leading companies to create higher profit margins on data they’ve obtained. Originally coined by a scientist at MIT, it involves “wrapping” tools with relevant data to boost its overall value. These tools can be B2B tools or consumer-facing ones. Examples include user dashboards that already contain a user’s interests or have data that will allow artificial intelligence to more easily determine the characteristics of users.

Only recently has data wrapping begun to be incorporated into commercial products. Though data and tools have certainly been sold independently for decades, this combination is novel. Though the joint use of the two was initially only intended for business to business or “B2B” programs, some consumer-facing portals have ended up using it, as well.

Who Came Up With Data Wrapping?

The MIT Center for Information Systems Research often publishes ideas for people and companies to better monetize their data. A research scientist working for this agency coined the term. Once they had defined it well and come up with a way to explain it to the public, the Center published a blog post about the practice here.

Why Is Data Wrapping Important?

The idea of simply rolling existing consumer data in with existing business analytics tools might seem obvious. After all, these tools are meant to take data in, process it in a meaningful way for a business, and put out reports based on the data. Though it’s likely that some companies already were informally performing this to further monetize products, the fact that a prominent institution coined this term carries weight.

Now that it’s formally recognized as a legitimate profit strategy, more firms are likely to adopt this model, specifically when developing software. It also signals the end of the “products for people” era of open-source software and ushers in the “Information Age” once and for all. Unlike much of software development, which focuses on the end-user and what they want in products, data wrapping focuses exclusively on improving internal business processes. Some companies have had ethical questions regarding data wrapping and even legal questions surrounding its influence, but the MIT publication attempts to answer some of these questions.

This isn’t to say that businesses haven’t utilized data wrapping to help their customers harness its power, though. For example, a prominent bank in 2016 took advantage of data wrapping to allow consumers to see all of their spending inside their bank portal. All of their credit card, loan, and bank account transactions could be seen in one spot. This simplification of finances makes it far easier for the average person to find success in the personal finance domain. As one of the first customer-facing uses of data wrapping in 2016, many other corporations followed suit, and this is now almost standard in the banking world.

How Do Organizations Leverage Data Wrapping Today?

Since around 2016, organizations have been trying to figure out how to maximize profits through leveraging data wrapping. These companies can make a cross-sectional team of people from their IT departments, acquisitions groups, and analytics groups within their companies.

These groups should then consider the needs of their business as well as their customers. We see this portrayed through the example of the all-inclusive banking portal. The bank foresaw customer utility in creating a compiled analytics dashboard for consumers.

The next step is internal implementation. This involves engineers and creative teams making pilot versions of these ideas. They should then be tested by the intended target audience. User experience feedback should then be harvested by the company to determine which data wrapping ideas hold the most promise.

Data wrapping has immense potential in the corporate world and has remained a game-changer when it comes to increasing the bottom line. Data science and software engineering intersect at just the right point to create yet more value in the world of technology and information.

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Conclusion

Why the Education Sector?

What Are the Potential Use Cases?

How to Become Data-Centric

What Is Data Wrapping?

Who Came Up With Data Wrapping?

Why Is Data Wrapping Important?

How Do Organizations Leverage Data Wrapping Today?

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