Your business wants your data to perform better. Quality data contributes to key perspectives for companies and has a huge effect on their decision-making. But where are you going to find reliable data? Although most of the data business uses is obtained from internal sources such as the software of ERP and CRM, some of it will come from beyond the network. In reality, the web is the biggest data archive out there.
The total amount of data in the modern world has risen dramatically, and there are no signs of slowing down. Experts claim it doubles in size every two years, from 4.4 zettabytes in 2013 to a projected 44 zettabytes (or 44 trillion GB) in 2020. However, these results are unstructured, unorganized and inconsistent. In order to truly capitalize on and collect useful insights, you need to effectively extract, plan and incorporate data so that it can be consumed on a scale. The data can’t only be reliable and usable. To deal with this scale, you need a trustworthy framework that handles external data with the same consistency and control as internal data sets. We'll move through several techniques to ensure data integrity and accuracy across the board to help your company. But first, here's some background to the consistency of the data.
What Is Data Consistency?
Data consistency ensures that there is clarity in the calculation of variables across datasets. This is particularly of interest as data is aggregated from several sources. Inconsistencies in the meaning of data between data sources can result in inappropriate, inaccurate datasets. Now, here are our favorite methods to verify that the data remains compatible with high-quality datasets.
Why Is Data Consistency So Important?
Data consistency may be the gap between a big business achievement or a huge disaster. Data is the basis for good strategic planning, and inaccurate data can result in misinformed business plans. Especially when aggregating data from various internal or external sources, it is important for businesses to ensure data accuracy so that they can be confident and effective in making business decisions.
Build the Sales Teams Guidance
A large volume of data is accessible to sales staff. But a lack of continuity in the handling of data among team members will easily lead to quality problems, so making sure everyone is on the same page is important. While much of the data will be obtained and automatically added, team members should also be informed on how to manually enter information. For example, they should complete all necessary fields, follow best practices, and use appropriate name and contact information formats. They can also realize that some data is going to be more useful than other data. For example, it could be better to have a lead's phone number than their email address; so, training the staff on how to prioritize data collection activities is wise.
Team participants can also monitor data quality on a daily basis. In order to avoid tainting your campaigns and damaging analytics, old data can easily become useless to your marketing and sales initiatives and should be discarded. Data should be checked regularly by dividing it into categories and ensuring that the information is comprehensive and correct in each group. Developing a data recovery plan is also critical. Accidents occur, and any substantial data loss may have devastating effects. Having a structured plan in place means staff members know how to react and minimize the damage in a worst-case scenario. This may involve backed up records on a cloud platform, providing a chain of command to be enforced by personnel, and introducing a formalized incident reporting method.
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Consolidate Disparate Web Sources Data
It's normal to upgrade or replace old systems. This can, sadly, generate holes where old systems do not match up with new ones, which can compromise the quality of data. Consolidation of the database is a solution that keeps data smooth and protects it from conflicting. This enables you to make a database that houses information for comparison from distinctly multiple sources and combines it with internal data. In addition, you are able to synthesize information so that it is simple to handle and has a degree of homogeneity. A crucial first step is to use a standardized operating system. Select a single platform and assure it is supported for all the applications and software you use.
"For compatibility to run in a single, centralized database, the workload should also be verified," states Hosting.com. Make sure that the hardware architecture will genuinely manage the workload of the consolidated database. Considerations provide specifications, among other parameters, for storage I/O, processing and memory. They can be configured so that they can be deployed on your OS if you develop your own applications in-house. Also, ensure adequate training on OS procedures for all administrators.
Normalize Data
Collecting data from multiple sources may result in variations in spelling and formatting. This confuses ERMs and CRMs, causes redundancies, makes it harder for leads to be segmented and generally pollutes the accuracy of your data. It is standardized by normalizing data, which ensures the degree of accuracy necessary for segmentation, lead scoring and more. For instance, say you're getting product details in different countries and numerous currencies from many different sources. Data normalization will allow you to place it in a single currency. Or say that you're struggling with bookings and availability where various calendar types are available on sites. To drastically simplify the material, you should bring all of it into a single date format.
The fundamentals of the approach include the creation of regular types that are listed from smallest to largest (e.g. 1NF, 2NF, 3NF, etc.). Each type follows unique rules that are structured to organize your database and clean up your work. For this resource, check out our Microsoft data science certification to learn the basics of data normalization and how it can be used by your company.
Automate Repetitive Tasks
Not only does automating data storage save time, but it also prevents many of the small mistakes that can affect quality and consistency. It is possible to automate several routine tasks, including:
- Data entry
- User input
- Validation
- Data field and mismatch updates
Using a UX-driven CRM that syncs with common applications and emails, for instance, to simplify user feedback and data entry. This makes it easy to import vital points such as their name, business name, telephone, email, and so on easily and efficiently in one fell swoop. For them, it's hassle-free, because the marketing and sales departments get the details they need to drive prospects through the sales funnel effectively. An example of validation for automation will be to ensure that data that is inserted into fields inappropriately is captured. For example, if a user mistakenly inserts their date of birth into an age field, an error message should be sent stating that there is a problem and advising them about what they need to do.
Processes like this maintain the integrity of data, where only specific information is received from the staff. For internal training, automating routine tasks is also essential, which provides a unified framework where new workers are right from the beginning on the same page. There is no guessing what template they are going to use.
Benefits of Routine Work Automation
Automation has some profound advantages. It really strengthens the lives of the customers and teammates. Customers don't have to enter form data meticulously and complete fields one by one. Instead, the software can collect key details, helping to push them more efficiently into the process. And less time can be wasted on the workers doing redundant, lower-level duties. Automation boosts their performance and removes much of the headaches that can come from data sorting across mountains. This will save the business money in return. As members of the team can expend less time on arduous, tedious data-related activities, they can concentrate on more important priorities, increasing their manpower.
It also facilitates collaboration between technology and your sales staff, as well as between customers and your sales team. Automating routine processes helps to seamlessly relay information to group members from software, which reduces much of the frustration that comes with manual data. It also lets the sales staff communicate better with customers, providing them with a detailed summary of order status, shipping details and so on. Beyond a compliance point of view, automation is helpful. It helps ensure confidential data is handled properly and minimizes the chances of unwanted third parties intercepting it. This can be a major advantage, with regulations such as the General Data Protection Regulation (GDPR) cracking down on mismanaged records. When you work with external data, using a WDI platform will provide a big advantage. Not only is it a huge time-saver and integral to communication, but it is also significant from a legal perspective.
Employ a WDI Strategy
It will contribute to useful insights as data is collected from the web. But it can be overwhelming to sift through the data. The challenge of collecting and transforming data, managing and securing data integrity, and adapting to increasing demands from company users and data analysts are also met by teams.
A modern approach to collecting and handling web data that emphasizes data accuracy and monitoring is high-quality web data integration (WDI). Using it helps the quick and repeatable processing of the capture and aggregation of website data, something that is important for companies looking to use online data on a scale or for important business processes.
But how can they use a WDI solution? Say you're looking for a competitive world to explore. You want to see where top players put themselves, and early on recognize changing behaviors, feelings and interests. In order to get a deeper understanding of what competitors are doing and how clients are reacting, WDI uses robust extraction, enabling you to view a wide range of site data, including displayed data, hidden data and extracted data.
Web data complements conventional market data, allowing you to keep up to date on strategic challenges. It enables you to synthesize comprehensive competitor data to facilitate the decision-making of your business. While it would be difficult on its own to absorb a vast amount of data such as this, a WDI organizes and handles it in a manner that helps you to quickly interpret and organize it so that you can achieve its maximum value.
Conventional "web scraping" methods that process HTML documents can have an immense volume of data, but it takes time and misses the big picture to digest the data. However, applying a robust WDI solution allows you to effectively retrieve, plan, incorporate and process the data. Not only do you have access to a wide range of information, but it is high-quality, meaningful and simple to apply for your company. It focuses on the performance and quality of results, which can have a huge effect on activities and generate a huge competitive edge.
Data Improvement for Successful Decision-Making
In several ways, poor data quality has a negative effect on your business. It not only results in lower decision-making, it can also be expensive. According to Gartner's study, the average financial impact of low data quality on firms is $9.7 million a year. So, to ensure data quality and efficiency, do everything within your energy. In these fields, the basic points identified here should give you lots of actionable ways to change. Internal data focuses on creating guidelines for the sales staff, consolidating files, normalizing data, and automating routine processes and external data focuses on using a WDI approach. For improved decision-making and improved profitability, the end result is clean, consistent data.
You perform only part of a full security and auditing strategy while you monitor data entry. When data is retrieved and changed, you must still check the results. Furthermore, you have to make sure the information is consistent. For high quality and holistic data set, Microsoft's Online Data Integration tools ensure that unstructured web data can be quickly retrieved, prepared and incorporated into the business operation. The path for Microsoft data science certification is structured into three levels: 1) Fundamentals, 2) Associate and 3) Expert.
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