Chance are you’re aiming to invest in a BI and analytics program to capitalize on the big data your company has been acquiring over the years. But before you spend millions on opting for expensive BI programs, take a step back and ask yourself three questions:
- Do I have data I can trust?
- Do I understand my data?
- Do I have a data transformation & data quality framework in place?
A, ‘No’ to these questions indicates that you need to optimize your data before you invest in a BI or analytics program. And this piece will help you understand how.
A Few Statistics to Push You into Action
Here are statistics from a survey conducted by the HBR in determining why most companies are failing in their data-driven efforts. The survey shows:
- 72% of survey participants report that they have yet to forge a data culture
- 69% report that they have not created a data-driven organization
- 53% state that they are not yet treating data as a business asset
- 52% admit that they are not competing on data and analytics
Alarming figures? In our experience of working with 4,500+ clients from across the globe, we know all too well the truth behind these statistics.
Organizations are ramping up their effort to be data-driven, but issues like the above such as the lack of a data culture, or the inability to treat data as a business asset make it difficult, for companies to be data-driven.
What is Data Transformation & Why You Need to Prioritize it Over Everything Else?
In an interconnected world, companies are dealing with an unfathomable amount of raw data. Imagine all the data you’re collecting from social media apps, marketing campaigns, sales campaigns, advertisements, market research activities, sales funnels and so on. All this raw data needs to be extracted, sorted, cleansed, and “transformed” into usable data giving valuable information.
Data transformation, therefore, is the process of transforming raw data into usable data. This process involves key steps as:
- Identifying the flaws affecting your data quality
- Integrating data from disparate sources into one consolidated source of truth
- Cleaning & fixing data (issues such as typos, missing values etc)
- Deduplicating data
- Mapping the data to a BI tool
- Making data usable for migration or other digital transformation purposes
Although this sounds simple in theory, in practice, data transformation is a hectic process that involves a significant investment in data transformation tools, consultation with third-party service providers and a buy-in from C-level executives. It takes at least a year of deliberation for a company to take the necessary steps it needs to transform data.
The Two Basic Approaches
Generally, there are two basic approaches to a data transformation solution. These are:
- The Manual Approach – Creating an In-House Team to Hand-Code ETL Solutions: A traditional method, this approach is still used by some organizations today, causing them to fail miserably. The data we have today is complex. It’s practically impossible to have a team of coders, creating ETL scripts for each data source.Not only is this a time-consuming process but also a counter-productive one. Teams have to spend months and years modifying scripts to match with increasing demand – yet failing to achieve the level of accuracy that is required for data to be efficient. Unintentional errors, misunderstandings, mundane and repetitive tasks make this approach an expensive failure for most organizations.
- The Software Approach – Getting an On-premises Data Preparation Solution: On-premises solutions allows companies to prepare, transform, integrate, and merge data from multiple sources into a new, master record. Compared to the manual approach, this automated approach takes place in a short amount of time, consumes fewer resources, is cheaper than hiring a full-fledged team, and requires only one person to manage the entire process. Some tools have an easy user-interface that allows non-IT users to match, clean & merge data without requiring any additional language expertise.
Five Types of Data Transformation that Your Data Would Need
Data transformation is a process made up of different processes and each process is designed to help businesses meet a certain data goal. For example, some businesses may already have a data cleansing mechanism in place but would probably need an integration solution to consolidate their data into one platform for obtaining a unique source of truth. Your data transformation needs are dependent upon your current data quality and your data goals.
Generally, if you don’t have a data quality framework in place, your data will need to undergo five basic processes to be transformed. These are:
Data Cleansing: Raw data is dirty data. In fact, any data that is collected by a system and has not been processed or analyzed for use tends to be dirty data.
When we’re talking about raw data it means any data that is:
- Plagued with spelling errors, typos, numeric & punctuation issues and much more.
- Duplicated several times in one data source or over multiple data sources (if an organization has multiple departments storing varying forms of information of an entity)
- Incomplete, inconsistent and inaccurate. Fake names, email addresses and physical addresses are some of the most common data quality problems.
Data cleansing is the first step in data transformation. You cannot do anything else until your data is cleansed of basic errors that give it a ‘bad health,’ indicator.
Data Deduplication: This is a classic problem with most organizations. It’s the most common problem we’ve had to encounter with Fortune 500 clients. A leading retailer, for example, had a troublesome time managing product data that arrived from multiple vendors and third-party dealers. With different unique identifiers, data formats, and data sources, product lists were badly affected by poor data quality.
Similarly, organizations that have customer data stored siloed away in multiple data sources often have problems with data duplication. If sales, marketing, billing are collecting the same customer data in three different ways, chances are data duplication will occur exponentially.
Data Standardization: Although the lack of a unified data format may not seem significant, in the long run, it causes the most severe bottlenecks during a data migration phase. If your new CRM has strict data standardization rules in place (such as all names must start with capital letters or all phone numbers must start with country + city code), you’ve got a serious problem to deal with. If the data in your organization is being collected and entered manually by different people using different formats, it will need to be standardized to be processed.
Seemingly inconsequential, data standardization is often missed out by organizations until they need to run a data matching activity only to realize that the data match algorithm misses out on information that does not have exact characteristics.
Data Validation: Is your source data accurate? Is it complete? For example, do you have accurate address data? Do you have more fake phone numbers and email addresses than valid addresses? Data validation is the process of ensuring that you have accurate, reliable data.
When moving data, it’s imperative that data from different sources conform to business rules of the new source or system and not become corrupted due to inconsistent data.
Data Consolidation: Data stored in disparate sources is one of the most critical challenges organizations face today. For an average enterprise to be connected to at least 400 applications, the amount of data streaming in is unfathomable. To make sense of all this data coming in from different sources and stored in different databases, companies need a solution that can let them merge or consolidate this data to get a single source of truth.
Data transformation is no longer an option – it’s the need of the hour (of the age?). For organizations that want to be digitally empowered and data-driven, they need to have data they can trust. This can only happen when companies shift their focus from investing in new cloud solutions and CRMs and instead focus on getting their data sorted. Without quality data, your digital transformation projects are bound to fail.
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