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Why companies must change their infrastructure for optimal data visualization

The digital revolution has brought a host of new opportunity and challenges for enterprises in every sector of business. While some organizations have embraced the wave of change and made efforts to be at its forefront, others have held back. These institutions may not have sufficient staff to implement the changes or may be waiting for a proven added-value proposition.

In other words: No technology upgrades until the innovation is proven useful. There is some wisdom in this caution. Several reports have noticed that productivity and efficiency are not rising at expected levels alongside this technology boom. However, as Project Syndicate noted, this lag may be a case of outgrowing the traditional productivity model, meaning that not every employee action is measured in the old system. 

However, there is another reason to explain why more technology does not automatically equal greater efficiency and higher profits. If a company buys some new software, it will see a minor boost. However, it will reap the full rewards only if staff properly learn to use said platforms.

Part of this problem stems from the misunderstanding that technology can only improve current work processes. This is not true. When looking at a basic enterprise operation like data visualization, technology has created an entirely new workflow.

Examining the traditional business model
In the traditional business model, all data visualization was manual. Employees would gather data from various endpoints and then input it into a visual model. Common examples of this process included pie charts and bar graphs. The employee would then present the data to the executive, who would use it to make information-based decisions.

While acceptable, this process is far from optimized. Most data had to be generated in spreadsheets before it was collected, using formulas made by staff. Collecting and framing the information is a time-consuming process that will absorb at least one individual. As employees are involved at every step of this workflow, there is a potential for human error.

The time involved prevented companies from acting on real time information. In the interim, intuition and "gut feeling" were used as substitutes for data-backed decisions. The people involved raised the level of risk that the data in question may be inaccurate or misleading.

Charts work because the human mind can understand pictures so much faster than words.Charts work because the human mind can understand pictures so much faster than words.

Unlocking data analytics 
Of course, with the arrival of the internet of things, companies have a lot more data collection at their disposal. Connected devices have provided a whole new network of information. This gold mine, also known as big data, has one downside: There is too much of it. A human cannot hope to categorize and analyze the information in any acceptable timeframe.

Enter data analytics. Using advanced technology like machine learning, companies can create and implement this software to study their data, creating automatic visualizations based on trends and prevalent patterns. According to Fingent, these software solutions employ mining algorithms to filter out irrelevant information, focusing instead on only what is important.

However, companies cannot simply go from a traditional system to a fully fledged data analytics solution for one reason: data segmentation. Many enterprises divide their information based on different departments and specializations. Each group works internally, communicating primarily with itself. While this method is helpful for organization, it greatly impedes data analytics potential.

If companies have siloed their data, the program will have to reach into every source, work with every relevant software and bypass every network design. In short, it will have to work harder to communicate. While modern data analytics solutions are "smart," they cannot navigate barriers like this easily. They are designed to optimally read only the information that is readily available.

For organizations to fully capitalize on the potential of internal data analytics, infrastructure overhaul is needed. Departments – or at least their data – must be able to freely communicate with one another. This process entails implementing a common software solution that is in use across the entire company.

The good news is that many modern solutions fit this need. Solutions like cloud platforms store relevant data in accessible locations and train employees to not segment their work. By creating an infrastructure that is open to the data analytics program, organizations can start to act on information, rather than relying solely on their gut. 

Data analytics can give companies real time answers to their challenges. Data analytics can give companies real time answers to their challenges.

How cloud infrastructure can help the retail sector

Cloud computing has caught on in a big way. A recent report from Right Scale found that 81 percent of the enterprise sector has adopted a multi-cloud system in at least some way. Public cloud adoption rates have continued to climb, as well, with the report noting that 92 percent of users now employ cloud technology (up from 89 percent in 2017). Across the board, cloud networks are gaining usership due to its improved interfacing, less dependence on in-house technical teams and flexible program structure.

However, some industry verticals continue to lag behind. The latest international Bitglass survey found that the retail sector has been slow to adopt cloud infrastructure. Only 47.8 percent of responding retail organizations had deployed the often-used Microsoft Office 365 suite, and Amazon Web Services – the most popular cloud system – was only used by 9 percent.

In short, retail is being left behind, and that lag is a serious problem for the industry – in part because retail is a sector that can profit immensely from successful cloud integration. However, cybersecurity concerns and technical knowledge limitations may be slowing down the adoption rate.

Taking advantage of mobile hardware
Almost everyone has a smartphone, that’s not an exaggeration. According to Pew research data, 77 percent of Americans have this hardware, and that number has been climbing steadily. Since smartphones are becoming cheaper and more user friendly, it is unlikely to think this device will be replaced in the near future.

Because smartphones are so ubiquitous and convenient, consumers are using them for a wide variety of tasks, including shopping. OuterBox found that, as of early 2018, precisely 62 percent of shoppers had made a purchase through their phones within the last six months. Another 80 percent had used their smartphones to compare products and deals while inside a store.

With a cloud infrastructure, retailers can better take advantage of this mobile world. Successful retail locations should consider maintaining at least two online networks – one for customers and another for employees. This setup will prevent bandwidth lag and help keep the consumer away from sensitive information. In addition, creating a mobile experience that is user friendly and seamlessly interwoven with the physical shopping experience is paramount.

Rather than building such a system from the ground up, retailers can take advantage of the numerous infrastructure-as-a-service cloud options available, leveraging a reliable third party rather than an in-house IT team.

Shoppers are already augmenting their experience with external online information. Shoppers are already augmenting their experiences with external online information.

Getting ahead of the latest trends
Data drives business intelligence, this is true in every enterprise sector. In retail, housing the right products can mean the difference between turning a profit and going out of business. However, retailers still using traditional sales reporting will be slow to react to shopping trends, as these reports can take months to compile.

Data analytics is the actionable side of big data. In retail, customers convey valuable information about shopping habits before they even enter the store, but if this data is not being captured, it is essentially useless. Bringing in an encompassing data analytics solution, which can read information such as store purchases, response to sales and even social media reaction, can provide retailers with extra information to make actionable decisions.

“This analysis removes the guesswork about what will sell and which styles will flop on the shelves,” Roman Kirsch, CEO of fashion outlet Lesara, stated in an interview with Inc. “We don’t just know which new styles are popular, we can also identify retro trends that are making comebacks, which styles are on the way out, and that helps us to precisely manage our production.”

Improving inventory management
In addition, data analytics can be paired with a responsive inventory management program. Retail-as-a-service solutions exist and can be used to track stock availability, shipping orders and in-store details. With this software, retail companies can get a real-time image of how well products and even entire locations are performing.

These solutions can prevent item shortages before they occur and give retail chains a greater understanding of performance at every location.

Using inventory management solutions can help retailers maximize their shipping profits. They can ship either directly to the customer or to the retail location most in need. Using inventory management solutions can help retailers maximize their shipping profits. They can ship directly to the customer or to the retail location most in need.

Concerning cybersecurity
Perhaps one of the factors slowing the adoption of cloud technology in the retail sector is cybersecurity. Retail organizations process multitudes of consumer credit information by the day, and the fallout from a data breach can be fatal in this sector. When faced with using cloud technology or in-house data center solutions, retail executives may believe that the safest hands are still their own.

However, this may not be the case. Research firm Gartner predicted that through 2022, 95 percent of cloud security failures will be the customer’s fault, meaning that issues will not come from a software defect but through poor implementation. The firm also concluded that cloud structures will see as much as 60 percent fewer cyberattacks than those businesses with in-house servers.

Cloud infrastructure is secure but must be installed and operated properly. The only thing that retail agencies have to fear when it comes to this new solution is technological ignorance, but many cloud providers and third-party services stand ready to aid in the installation process.

How a holistic approach to data analytics benefits cybersecurity

Almost everyone, regardless of industry, recognizes the growing importance of cybersecurity. Cyberattacks are on the rise and growing increasingly varied and sophisticated. According to data collected by Cybersecurity Ventures, the annual cost of cybercrime is estimated to reach roughly $6 trillion by 2021. An effective information security policy is, in many cases, the only thing standing between companies and possible financial ruin.

The danger is especially real for small- to medium-sized businesses. Data from the U.S. Securities and Exchange Commission found that only slightly more than a third of SMBs (40 percent) survive for longer than six months after a successful data breach. For these types of organizations, cybersecurity is literally a matter of life and death.

The good news: Many businesses recognize the need for effective cybersecurity strategies and are investing heavily in personnel and software solutions. The bad news: Many of these same companies are only reacting, not thinking about how to best deploy this protective framework. Effective cybersecurity isn’t as simple as applying a bandage to a cut.

It can be better equated to introducing a new nutritional supplement to the diet. The whole procedure is vastly more effective if integrated into every meal. To best use modern cybersecurity practices, businesses must rethink their approaches to corporate data structure. Data analytics is a vital tool in providing the best in information protection.

“Segmenting data spells disaster for an effective cybersecurity policy.”

Siloed data is unread data
As organizations grow, there is a tendency to segment. New branches develop, managers are appointed to oversee departments – in general, these groups tend to work on their projects and trust that other arenas of the company are also doing their jobs. The responsibility is divided and thus, easier to handle.

While this setup may make the day-to-day routine of the business easier on executives, it spells disaster for an effective cybersecurity policy. This division process creates siloed or segmented data pools. While a department may be very aware of what it is doing, it has far less knowledge of other corporate branches.

Many organizations may figure that an in-house IT team or chief information security officer can oversee everything, keeping the company running at full-tilt. However, this assumption is only half-true. While these staff members can and do oversee the vast majority of business operations, they will lack the data to make comprehensive decisions. A report from the Ponemon Institute found that 70 percent of cybersecurity decision-makers felt they couldn’t effectively act because of a surplus of jumbled, incoherent data.

Data analytics, or the study of (typically big) data, provides facts behind reasoning. To gather this information, companies need systems and software that talk to one another. Having the best-rated cybersecurity software won’t make a difference if it can’t easily communicate with the company’s primary OS or reach data from several remote branches.

CISOs or other qualified individuals can make practical, often less-expensive strategies with a clear view of the entire company. Without this type of solution, a business, no matter its resources or personnel, will essentially be operating its cybersecurity strategy through guesswork.

Separated data creates bubbles where information can be misplaced or duplicated, resulting in a slower data analysis process. Separated data creates bubbles where information can be misplaced or duplicated, resulting in a slower data analysis process.

Centralized businesses may miss real-time updates
Businesses face another challenge as they expand. Data collection has, in the past, slowed with remote locations. Before IoT and Industry 4.0, organizations were bound with paper and email communications. Remote branches typically grouped data reports into weeks or, more likely, months.

This approach meant that the central location effectively made decisions with month-old information. When it comes to minimizing the damage from data breaches, every hour matters. Luckily, many institutions can now provide data streaming in real time. Those that can’t must prioritize improving information flow immediately. Cybercrime looks for the weakest aspect within a company and tries to exploit the deficiency.

For data analytics to work properly, businesses need access to the full breadth of internal data. The more consistent and up to date this information is, the better CISOs and IT departments can make coherent and sensible decisions.

Visibility may not sound like the answer to fighting cyberattacks, but it is a crucial component. Companies need to be able to look within and adapt at a moment’s notice. This strategy requires not just the ability to see but also the power to make quick, actionable adjustments. Those organizations that still segment data will find this procedure difficult and time consuming.

As cybercrime becomes an expected aspect of business operations, those who still think in siloed brackets must change their mindsets or face expensive consequences.