black and white business building business owner walking up stairs black and white hand on desk for underwriting services from LQD Business Finance small business owner standing under bridge black and white black and white business meetings for working capital financing solutions Contact Us Header

Developing an Efficient Data Architecture

With worldwide IT spending projected to total $3.6 trillion in 2020, data analytics has morphed into one of the most influential elements of business operations today. Tech-enabled companies like LQD Business Finance rely on proper data analytics and management systems for overall operational efficiency. Accurate data provides vital measures of past and present performance for a company, and more importantly, it informs future decisions that dictate long-term success.

Data science plays a significant role in almost every aspect of a business, such as customer service, inventory management, sales, and much more. Consequently, data management is a strong indicator of operational efficiency, wherein better data leads to higher levels of efficiency by reducing the time, labor, and cost necessary to run a business.

However, the sheer volume of data available to any given company is astronomical, so how do you determine the right strategy and system that benefits you the most? Knowing what kind of data to collect is only the beginning; data must be properly stored, analyzed, and integrated for it to be of adequate use for your company. Otherwise, data can do more harm than good by misleading and overloading your business with irrelevant information that is expensive and time-consuming to manage.

Establishing a proficient data architecture for your company should be a top priority in managing your operations. Here are five of the biggest mistakes in data technology and management that hurt operational efficiency, and how you can avoid them.

5 Data Mistakes to Avoid for Operational Efficiency

    1. Gathering the Wrong Kind of Data

      A common approach that businesses take when figuring out what kind of data to collect is ‘bigger is better,’ but that is not necessarily the case. Bigger volumes of data may sound appealing, but this can flood your database with extraneous information that wastes time and resources and dilutes the impact of the data.

      Instead of focusing on gathering as much of it as possible, the priority should be to ascertain information that directly supports your business goals. To do so, you must first clearly outline specific and measurable objectives for your operations, which then influence the breadth and scope of the data you need to collect. If you do not know what questions you are trying to answer with your data beforehand, then you will not get meaningful answers.
      Your data collection should also consider long-term development and ROI, rather than just meeting short-term targets. Without a balance between short-term function and long-term marks, you expense a lot of effort for a limited purpose.

      Additionally, it is important to distinguish the value between big data and small data. The two vary not just in volume, but also in specificity. Big data is helpful for overarching evaluations of your company, like gaging brand awareness or calculating annual reports. On the other hand, small data is more suited to precise business targets, like assessing individual customer behavior. With such distinctions in mind, an effective approach often involves a deliberate combination of big and small data to paint the most accurate picture possible for making business decisions.

    2. Misinterpreting Data

      Once collected, data lacks practicality without interpretation and contextualization. A grave misstep companies often take is not investing in methods of skillfully analyzing data after it is amassed. For data to be conclusive, businesses should pull it from multiple sources and refine it with company-specific parameters to generate insight that is relevant and expedient.

      Before it is analyzed, your team should clean up any “dirty” data to prevent faulty calculations. This includes deleting redundancies, factoring out errors, and checking for any missing pieces of information that may negatively impact the quality of the analysis. Be sure to note any patterns in the “dirty” data so they can be addressed in subsequent processes to reduce the rate of repetitive mistakes.

      Data analysis should outline the purviews of the information and distill it into manageable pieces. For example, if you conducted a customer survey for a product, your analysis should present quantitative figures like average ratings or rate of response, as well as qualitative facts like commonly recurring complaints.

      To define the criteria of your analysis, return to the objectives laid out when deciding what kind of data to collect. You should have identified KPIs that reflect your personal measures of success, and the results from your analysis should reveal whether or not those KPIs were hit. If your analytics are unable to answer the questions that you set forth with your objectives, adjust your data architecture by steering it back towards your goals.

    3. Failing to Provide Analytics Training

      Data management is nothing without the operational structure to support it, so it is crucial to provide analytics training for your employees to learn how to use data. All of your employees, regardless of department or role, should understand the scope and function of the data that drives your operations, including how to access and appropriately handle it.

      Not training employees on data management and analytics leads to miscommunication and inconsistencies that hinder performance. Proper onboarding may take additional effort upfront, but as a result, your teams are much better equipped to take advantage of the data at their fingertips. Your ability to maximize the power of data relies on your employees’ capacity to interact with it in a way that positively informs their responsibilities and improves the caliber of your decisions.

    4. Lacking Authority over Data

      Another major failing point is not clearly designating roles and responsibilities over data management. Within an organization, a specific person or team must be assigned central authority to assure the accuracy and quality of all the data that flows throughout the company. Typically, central authority falls on CIOs and IT teams, and they should be responsible for essential tasks like maintaining data hygiene, enforcing cybersecurity, and conducting analytics training.

      Having specialized personnel for data technology allows your company to evolve alongside the data because they can facilitate a feedback loop in the data architecture to identify issues and patterns for functional improvements that increase operational efficiency. They can also customize and filter data to the particular needs of your company or a distinct initiative.

      Beyond central authority, everyone else in the organization that interacts with the data must also be held accountable for adhering to the guidelines of the data architecture. Given the widespread applications of data within a company, it is more likely than not that data is shared between departments. For efficient interdepartmental collaboration, there needs to be consistency and transparency across the board, and this is difficult to maintain without a designated person or team to enforce it.

    5. Exposing Data to Cyberthreats

      As technology continues to progress, unfortunately so do the capabilities of cybercrime.  From phishing to identity theft to ransomware, the threat of cyberattacks has risen dramatically as more and more businesses modernize their operations. However, a staggering number of small businesses do not take enough precautions. In 2019, almost 50 percent of cyberattacks were aimed at small businesses, but only 14% were prepared to defend themselves. Moreover, 62% of small businesses believed that a cyberattack was not likely.

      The COVID-19 pandemic has also drastically increased the rate of cybercrime against businesses. With so many vulnerable companies needing to mobilize their workforce and accommodate a work-from-home structure, a recent report showed that 70% of IT and cybersecurity professionals who were surveyed expect the pandemic to necessitate significant changes in IT and security in 2021.

      Not protecting your data against cyberthreats is one of the most damaging mistakes you can make. Last year, a cyberattack was estimated to cost a business an average of $200k, with 60% of companies going out of business within six months of being victimized. While no business can make themselves threat-proof, you can greatly decrease the chances of becoming a target by taking proactive caution.

      Endpoint protection products and next-generation firewalls are some of the most effective methods of improving cybersecurity. Other practices that will help prevent an attack include utilizing VPNs and implementing multi-factor authentication. Another critical measure to take is educating employees on cybersecurity, as a primary cause of cyberthreats is employee blunders like opening suspicious emails or using weak passwords.

Overall, data management demands a great deal of skill and caution, but countless benefits make data technology a rewarding investment that allows companies to improve efficiency, generate profit, and stay ahead of the competition. Developing and applying the right data architecture is an imperative element of growth for a business in the digital age, and with the right strategy, it will prove to be a longstanding asset for your company.

  • Blog