What is data harmonization?
Data harmonization is the method of unifying disparate data fields, formats, dimensions, and columns into a composite dataset. In order for an organization to succeed, users need democratic access to clean high-quality data and conformed dimensions—data complexity is hidden, and data formats are agreed upon.
All data-related sciences can be harmonized and used effectively for the good of the company, from data engineering to applications to data analytics. A relatively new approach to data analysis and visualization, data harmonization allows users to channel data from different sources into a consistent, standardized, and comprehensive format for analysis.
Data harmonization takes information from diverse sources, clears away errors, and presents the resultant data as an accurate body of information. The user can effectively and efficiently access a single window of crucial knowledge to facilitate informed decision-making. When data is disparate and unaligned, it is difficult to process, extract insights, and identify pain points. But once data is cleaned, sorted, and aggregated—in short, harmonized—it can provide the user with a complete picture.
How does data harmonization benefit businesses?
In simple terms, data harmonization increases the value and utilization of data. Data harmonization also makes it possible for organizations to transform fragmented and inaccurate data into workable information—creating new analyses, insights, and visualizations. This means that data harmonization helps the user reduce the time taken to access business intelligence, discover key insights, and detect early disruptions. It also significantly lowers the overall cost of complex data analysis and the cost of handling data in the long run. If an organization is spending less time scrambling to find the right source of data, then it can spend that time more effectively elsewhere, such as in growing the business and making a significant revenue impact.
Whether an organization has been around for several decades or is a recent start-up, it will inevitably gather a plethora of data. Along with it, there is the distinct possibility that the enormous array of information gathered from a wide variety of sources will have errors and misinformation. Besides this, the sheer volume of information collected over a company's lifespan can be unwieldy and overwhelming.
With data harmonization tools, this data can be a valuable mine of insights and business intelligence. Organizations can learn things about their customers, changing market forces, and even insights about competitors. The good news is that every company across the globe is mining and storing data to make smart business decisions and manage their customers. But first, to make sense of all that data, organizations need to harmonize it.
Most companies spend huge amounts of time and resources on commissioning surveys, conducting focus group sessions, and gathering information from the internet, news channels, and social media networks. All this information does not come together in one manageable, cohesive body but rather as a mish-mash of raw data. To make sense of it as a whole, it needs to be harmonized. Raw, unharmonized data isn’t suitable for business analysis. It often contains irrelevant pointers, misleading values, and duplicate statistics. However, when organizations use data harmonization techniques, they can standardize data and create a single source of verifiable information.
How to build confidence in an organization’s data
When an organization aligns, verifies, and clears inconsistencies in its data mine, the data can then be interpreted successfully and used with confidence. Clean, verifiable data is very important for confident decision making and strategizing within a company. But without the ability to guarantee the source or veracity of the data, any business should hesitate to make crucial, tough, or timely decisions with it because it could adversely impact the business as a whole. It can prevent important decision making and hamper the organization's growth trajectory. All this will negatively impact revenue and possibly lead to ill-timed choices, a drop in production, and possible loss of overall market share.
Why is a single source of truth critical for business success?
The amount and variety of data that is churned out and stored daily is increasing at an alarming pace, especially as the use of handheld, wearable technology and other Internet of Things (IoT) devices continue to grow exponentially. This massive amount of data needs to be gathered, siloed, and formatted into a clean, consistent body of information for it to have significant value.
Through effective data harmonization, organizations can better understand where they are headed, stay ahead of the competition, and position themselves to withstand disruptions. Access to high-quality, cleansed data allows an organization to analyze marketing efforts and sales, identify pain points, and discover other aspects that contribute (or detract from) its success.
As companies and individuals learn to manage their information and their data successfully, they can put themselves in a winning position. When an organization has the ability to access regularly updated data, it saves time on reverifying multiple data sources. This enables management to easily identify business intelligence insights and make the company more agile and responsive to market changes and triggers. Data harmonization:
- Supplies data in a form that allows an organization to analyze it both internally and externally
- Creates democratic access to hierarchies that enable broad views across data sources
- Provides enough information for data-based decisions without too much overwhelming detail
The steps for data harmonization
Once the master data is in place, it can be used many times across a range of departments to harmonize each department’s data continually. With incremental data updates, the quality of the master data will also improve. Having a single source of accurate data means teams and departments don’t have to develop their own datasets, which can be expensive, prone to error, and conflicting. Teams from different departments within the organization such as marketing, sales, HR, and operations can benefit and use one harmonized data set.
Step 1: homogenize and organize the data
Data is usually gathered from a range of sources, and each data origin point has its own unique structure and format. Step one is to homogenize all the data into the same format and then create hierarchies.
Data alignment means there is a standard language and hierarchy for:
- Time frames
- Geography (including countries, states, regions, and cities)
- Customers, transactions, reviews, and feedback
- Macroeconomic and key performance indicators (KPIs)
- Advertising and sales campaigns
Step 2: create and build an information model
Once data is successfully harmonized, it is integrated into an aggregate information model, which allows users to access both an overall view and smaller details for specific products. This model, which is in a regular and logical format, allows an organization to detect correlations between data categories, from advertising to sales to production to distribution.
By creating metrics for future analysis, users can detect anomalies before the data is sent to analysts.
Step 3: transformation
Extract, transform, load (ETL)
To move data into a common dataset, users require three operations: extraction, transformation and loading (ETL). Extraction gathers the data in the original dataset; transformation changes the format so it is ready for analysis; loading writes the data to the destination dataset. ETL can cause major issues in the final data integration process because just one error can throw the whole system out of sync.
Data virtualization creates a layer where applications can access, retrieve, and manipulate data as needed. It brings all the information together in one virtual location, allowing real-time access with no need to perform ETL. Data virtualization is often more cost effective and accurate.
The right technology is important in the harmonization process; analysts and data scientists should focus less on micromanaging data movement and instead focus on adding value to the business by deriving insights and value from data.
Step 4: data cleansing
Data cleansing is correcting or removing inaccurate, faulty, or inconsistent data from a dataset. It’s almost as if the analyst is giving the data a complete overhaul or makeover. Checking and eliminating misspelled names or removing duplicate fields are examples of data cleansing.
Step 5: data normalization
Data harmonization and normalization are interchangeable. They both work towards making the basic aspects of data the same. For example, enabling a tweet and a video (which have different formats) to exist in the same dataset with compatibility.
Step 6: classifications
At its most basic, classifications allow users to segment the data, filter it as they require, and extract whatever information is necessary, similar to the headings at the top of an excel spreadsheet.
Not harmonizing business data could be risking a lot
If there’s no data harmonization, business strategists will not have an authentic picture of trends, sales, and other essential business measurables. It will also be hard to see an overall view of data or drill down to gain micro insights. Most importantly, management can miss opportunities or potential disruption because the data is widespread, disorganized, and in a range of disparate forms. This means that organizations may make erroneous and possibly expensive decisions, lose sales, and potentially even risk the entire company.
Best practices of data harmonization
Good data harmonization is generally a mashup of automated tasks and manual techniques. This mixes calibrated artificial intelligence (AI) and the work of skilled data scientists, so that over time, a large percent of the overall process can be automated. By maximizing AI, the risk of errors is greatly reduced, and the length of time to derive insights is much faster.
Smart data models should be built that will meet future demands, not just today’s immediate needs. By tapping into industry and sector expertise, data harmonization can save time and allow data analysts to harmonize their data directly.
One of the biggest challenges in managing data for an organization is mapping the data and clearly understanding how and when their various data sources will interact with the existing data infrastructure. This becomes a formidable task if the team is inexperienced in this rather niche segment and if they are not sure about which tools to use.
Is It data harmonization or master data management?
Master data management (MDM) is a system that targets the efficient management of communal master data accessed by diverse information technology groups and systems. It provides democratic access to an organization’s centralized data cache. It also solves data problems by focusing on streamlining business processes, data quality, and the holistic integration and standardization of information systems.
Data harmonization takes this one step further and cleanses data to remove inaccuracies and inconsistencies from across the range of sources. It attempts to create harmony between various data sources to build a cohesive, complete picture. This data harmonization system is like putting pieces of the puzzle together to create a logical picture. This is done by identifying, cleansing and processing the different data components and variables.
How is data harmonization critical to an organization’s success?
Data harmonization can be an exhaustive, time-consuming process. However, it should be a seamless step of the whole analytics process, so that management can actually focus on finding the insights that drive a business forward.
But if the data is incomplete or erroneous—in other words, not harmonized—organizations are forced to look at their sources of data separately. That is counterproductive to the process of smart decision making, since market intelligence spans channels, devices, and people. Even the sources where customer data is collected are eclectic, evolving, and rapidly changing, with certain tools becoming redundant and replaced every day. Having a centralized data model—that utilizes machine learning-powered harmonization—makes this process simpler.
Data harmonization is the future of business management. Its ultimate goal is to complement and support efficient data processing to ensure smart decision making within the organization. In the coming years, data harmonization will become a prerequisite to ensure business efficiency and overall organizational success.