Category: Big Data

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.

Effective use of data has become more important to modern businesses than many could have imagined a decade ago. As a piece on why every company needs a data strategy back in 2019 put it, data now “matters to any company” and is “one of our biggest business assets” in the modern environment. These are indisputable statements at this point, and they’re why every business hoping to succeed today needs to modernize its data platform (if it hasn’t already).

That said, even among those who like the idea of embracing data, many don’t quite understand what modernizing means in this sense. In this piece, we’ll look at why this needs to be done, who needs to do it, and what, ultimately, the process entails.

Why Modernize Data?

In very general terms, we addressed the why above: Effective data usage is indisputably one of the greatest assets available to businesses today. More specifically though, the role of data in business comes down to insight across various departments and operations. A robust data operation allows companies to understand needs and develop detailed processes for hiring; it enables marketing departments to make more targeted and fruitful efforts, and it helps management to recognize internal trends that drive or detract from productivity, and act accordingly. Modern data essentially streamlines business and makes it more efficient across the board.

We would also add that for smaller businesses, the why comes down to competition. The democratization of data in modern times is giving smaller companies the means to match larger competitors in certain efforts, and thus giving them a chance to keep pace.

Who Modernizes Data?

The answer to who brings about data modernization within a company will vary depending on the size and resources of the company at hand. For smaller businesses or those with particularly limited resources, it is possible to make this change internally. Much of the data modernization process comes down to using tech tools that can gather and catalog information in a largely automated fashion.

At the same time though, companies with more resources should consider that data analytics is a field on the rise, and one producing legions of young, educated people seeking work. Today, countless individuals are seeking an online master’s in data analytics specifically on the grounds that the business data analytics industry is in the midst of a projected 13.2% compound annual growth rate through 2022. Jobs in the field are on the rise, meaning this has become a significant market. This is all to say that it’s reasonable at this point for businesses seeking to modernize their data operations to hire trained professionals specifically for this work.

What Should Be Done?

This is perhaps the biggest question, and it depends largely on what a given business entails. For instance, for businesses that involve a focus on direct purchases from customers, data modernization should focus on how to glean more information at the point of sale, build customer profiles, and ultimately turn advertising into a targeted, data-driven effort. Businesses with large-scale logistics operations should direct data improvement efforts toward optimizing the supply chain, as Inzata has discussed before.

Across almost every business though, there should be fundamental efforts to collect and organize more information with respect to internal productivity, company finances, and marketing. These are areas in which there are always benefits to more sophisticated data, and they can form the foundation of a modernized effort that ultimately branches out into more specific needs. 

At that point, a business will be taking full advantage of these invaluable ideas and processes.  

Written by Althea Collins for Inzata Analytics

One of the biggest challenges facing many companies is figuring out how to optimize their supply chains. For obvious reasons, they want to strike a balance between keeping costs down and making sure they have the resources required to continue to operate. As became evident during the early months of the COVID-19 outbreak, supply chains, especially global ones, can be tricky beasts to tame.

Maintaining the right balance between efficiency and resilience is challenging even in the best of economies. One solution many enterprises now use to stay nimble in the face of evolving circumstances is Big Data. 

By using computing power, algorithms, statistical methods, and artificial intelligence (AI), a company can condense the massive amount of available information about supply chains into comprehensible insights. That means making decisions quickly and without sacrificing optimization or resiliency. Let’s take a closer look at this trend and what it might mean for your operations.

What Can Big Data Do?

Computing resources can be focused on a handful of supply chain-related issues. These include jobs like:

• Forecasting supply and demand
• Proactive maintenance of infrastructure elements like warehouses and transportation
• Determining how to best stow freight
• Making pricing and ordering decisions
• Inspecting items and identifying defects
• Deploying workforce members, such as dockworkers and truck drivers, more efficiently

Suppose you run a consumer paper products company. You may need to scour the world for the best total price for a wood sourcing shipment. This may mean using Big Data systems to collect information about prices down the road and halfway across the world. Likewise, the company would need to make decisions about whether the costs of transporting and storing the wood pulp would be effective. Similarly, they’d need to establish confidence that each shipment would arrive on time.

How to Build the Needed Big Data Resources

First, it’s critical to understand that taking advantage of big data is about more than just putting a bunch of machines to work. A culture needs to be established from the top down at any organization. This culture has to:

• Value data and insights
• Understand how to convert insights into actions
• Have access to resources like data pools, dashboards, and databases that enable their work
• Stay committed to a continuous process of improvement

A company needs data scientists and analysts just as much as it needs computing power. C-level executives need to be onboarded with the culture, and they need to come to value data so much that checking the dashboards, whether it be on their phones or at their desk, is a routine part of their duties. Folks involved with buying, selling, transporting, and handling items need to know why supplies are dealt with in a particular way.

In addition to building a culture, team members have to have the right tools. This means computer software and hardware that can process massive amounts of data, turn it into analysis, and deliver the analysis as insights in the form of reports, presentations, and dashboards. Computing power can be derived from a variety of sources, including servers, cloud-based architectures, and even CPUs and GPUs on individual machines. 

Some companies even have embraced edge intelligence. This involves using numerous small devices and tags to track granular data in the field, at the edge of where data gathering begins. For example, edge intelligence can be used to track the conditions of crops. Companies in the food services industries can then use this data to run predictive analysis regarding what the supply chain will look like by harvest time.

What Are the Benefits?

Companies can gain a number of benefits from embracing Big Data as part of their supply chain analysis. By studying markets more broadly, they can reduce costs by finding suppliers that offer better rates. Predictive systems allow them to stock up on key supplies before a crunch hits or let slack out when the market is oversupplied. Tracking customer trends makes it easier to ramp up buying to meet emerging demand, driving greater profits.

Developing Big Data operations separates good businesses from great ones. With a more data-driven understanding of the supply chain, your operation can begin finding opportunities rather than reacting to events. By putting Big Data resources in place, supply chain processes can become more optimized and resilient.

Closely Monitor Adoption

It’s no secret that people are hesitant when introducing new tools and processes. If you don’t keep a close eye on the transition to a new tool, users will likely continue to use outdated methods such as disparate and inaccurate spreadsheets. Make sure those involved are working with the solution frequently and in the predetermined capacity. If you notice a few individuals rarely using the tool, reach out to discuss their usage as well as any concerns they might have surrounding the business intelligence solution. 

Top-Down Approach

Another strategy to increase user acceptance is the top-down approach. Buy-in from executives and senior stakeholders is crucial to fostering adoption, whether it be throughout your team or the entire organization. 

Consider bringing on an executive to champion the platform. This will empower other end-users to utilize the tool and recognize its overarching importance to the business moving forward. Leadership should also communicate heavily the why behind moving to a new solution. This will align stakeholders and help them to understand the transition as a whole.  

Continuous Learning & Training

Training is key to the introduction of any new processes or solutions. But you can’t expect your employees to be fully onboarded after one intensive training session. Try approaching the onboarding process as a continuous learning opportunity.

Implement weekly or bi-weekly meetings to allow everyone involved to reflect on what they’ve learned and collectively share their experience. Additionally, allotting time for regular meetings will give people the chance to ask questions and troubleshoot any possible problems they’ve encountered. 

Finding Data that Matters

Demonstrate the power of using data to drive decision making by developing a business use case. This application will allow you to establish the validity of the BI solution and show others where it can contribute value across business units. Seeing critical business questions answered will assist in highlighting the significance of the tool and potentially spark other ideas across users.

Remove Alternatives

A more obvious way to increase adoption is to remove existing reports or tools that users could possibly fall back on. Eliminating alternatives forces users to work with the new solution and ultimately familiarize themselves with the new dashboards.

Conclusion

Overall, there are many effective strategies when it comes to increasing user adoption. The downfall of many companies when it comes to introducing new solutions is their focus on solely the technical side of things. The organizational change and end-user adoption are just as crucial, if not more important, to successful implementation. Consider these approaches next time you’re introducing a new business intelligence solution. 

A relational database is a type of database that is based on the relational model. The data within a relational database is organized through rows and columns in a two-dimensional format.

The relational database has been used since the early 1970s, and is the most widely used database type due to its ability to maintain data consistency across multiple applications and instances. Relational databases make it easy to be ACID (Atomicity, Consistency, Isolation, Durability) compliant, because of the way that they handle data at a granular level, and the fact that any changes made to the database will be permanent. SQL is the primary language used to communicate with relational databases.

Below is an example of a two dimensional data array. Each axis in the array is a dimension, and each entry within the dimensions is called a position.