Data blending – think of it as a data mash up – addresses a difficult issue that faces many companies. Since companies have now been gathering data for several years – or longer – they now often have dozens of data repositories, from Excel spreadsheets to Tableau workbooks. Each of these bodies of data offers potential value, if properly data mined for business insight.
By wrangling disparate data into a single data set, data blending can create a single source to feed into Big Data software all at the same time. Importantly, data blending can help uncover compelling correlations between apparently contrasting data sets.
Data blending’s real advantage: data blending tends to be a fast data mining process that sales reps and business analyst can use for specific queries, without needing help from IT support staff.
Data blending can help merge disparate data quickly on a per project basis.
Advantages of Data Blending
The plethora of data sets to potentially data blend into a single is set is nearly exhaustive.
This may include traditional data bases, CRM systems, HR, user generated data from forms, social media, marketing operations, Web analytics, and usually includes a free mix of both structured and unstructured data.
Data blending is, to be sure, is not without its costs. Staff must spend time gathering and routing data from a variety of sources. Data blending clearly requires investment in staff time. Additionally, some data lakes may be harder to blend into a single pool than others. This can offer thorny administrative challenges in data handling.
However, in a world that’s drowning in information, data blending enables the following key competitive advantage in data mining.
- Offers faster, more accurate access to critical data, allowing a business to glean insights more quickly.
- It empowers greater efficiency for all types of data action, from straight data mining to advanced predictive analytics.
- It allows a far higher quality of data intelligence to executives and the data scientists who work with them.
- Ultimately, it enables significantly better decision making because the data that drives decision making is better organized and more logically located.
Data blending tools can make data merging a far faster process. Source: Google.
Steps to Data Blending: Gather, Combine, Join
Some businesses are reluctant to do the complex work of data blending. After all, each of a company’s division often has its own system – and formats, and labeling – for handling and storing data. Depending on the job, even a specific, project-based data blend can be a cumbersome and time-consuming process.
However, if the process of data blending is broken down into its component parts, it is far more manageable. So while there can be many aspects (permission issues, hunting down the data), in the global view, data blending is a three-step process.
- Data Gathering: Explore, label and quantify all the many data sets required. Naturally, the more thorough the data gathering, the most insights to be gleaned from the resulting data set.
- Data Combining: Once the myriad data are culled, join these disparate data sets into one central data set, data lake or – for big, ongoing jobs – data warehouse.
- Data Cleansing/Purging: In some cases, the data will need to be transitioned into a format that allows it to be stored in single repository. Additionally, once all the nooks and crannies of data have been examined, it’s likely that some of the data will need to be purged; it simply is not usable or relevant to the company’s larger mission, so it just slows down the overall data mining process.
Data Blending vs. Data Integration
The terms data blending and data integration, like many terms in the evolving world of data analytics, are used differently by different people.
The short comparison: data blending is typically a faster, more project-based merging, while data integration is usually a more full-fledged merging of data sources.
Let’s go in-depth:
Data Blending: for Projects
As noted above, data blending is based on merging multiple sources of data into one data set, a process that may (or may not) require preparation or reformatting of the data. The emphasis it typically on speed, with data being blended for a specific project or business query that is time sensitive.
Data blending tools are used, so that IT staff are not required.
However, to add complexity to the definition, in some scenarios companies are data blending to produce an ongoing single repository – not for a single use case – that will be used to query the data as market conditions change.
Data Integration: the Single View
Data integration – confusingly – also refers to combining data from multiple sources. If there’s a key differentiator between data blending and data integration, it’s that data integration offer a single unified view of the data.
Producing this unified view typically requires some deep reformatting of the underlying data, so trend lines can based on apples-to-apples comparisons. In some cases, data virtualization is required.
In short, data integration tends to be a deeper, more complex process than data blending.
Data integration typically involves extract, transform and load (ETL), which is a process that businesses have used for decades.
Data Blending, ETL and ELT
While data blending is seen as a very contemporary process, necessitated by the complexity and number of today’s multiplying data sources, in fact it follows a legacy historical process.
Note, for instance, the similarities of the process above with ETL:
- Extract: Gather data from multiple sources.
- Transform: Change data formatting as required to mix it with other disparate data sources.
- Load: Transfer the newly formatted data to a single data repository.
One of the complaints about the ETL process is that it typically requires IT staff. This can mean that these initiatives are not as nimble and project-focused as a system that is managed by data scientists, or the business analyst that actually design and create Big Data queries.
However, as the pace of business has increased – and the number and complexity of inquiries has multiplied exponentially – businesses have shifted toward ELT, which is a form of data blending. ELT, in contrast to ETL, is a process that runs usually without needing support from IT staff. It looks like this:
- Extract: Gather data from multiple sources.
- Load: Organize the data – which may or may not require reformatting – into a single location.
- Transform: Merge the disparate data sources to the extent a project calls for, given the needs of that specific query.
In other words, ELT – a key form of data blending – is more nimble and query based, producing faster, more specific business insights from data mining.
Data Blending Tools
Although “data blending” is seen as a separate technique from the overall process of data analytics, in fact data experts mix and match data as a constant part of the data analytics process.
In fact, given that the torrent of data is growing at an exponential clip, the act of data blending from various sources is likely one of the most common acts for today’s data scientists.
Note: data blending can be an in inexact process. Realize that in some cases, as desperate data is merged, some aspects of the data may be included. When data is blended “on the run,” users may simply viewing disparate data side by side.
In this case, merely aggregating the data into a single view is helpful, to be sure, but will not enable the kind of rich data mining as would a true data transform-and-merge process.
Which brings us to data blending tools. While plenty of “data blending” is no more advanced then copying and pasting Excel columns, a good data blending tool tends to pay for itself quickly.
These applications below are among the leading tools used in data blending jobs:
- Clearstory Data
- Altair Knowledge Works