In this day and age of online presence building and digital marketing, data teams operate in data stacks that neatly set aside the relevant data in relevant fields. But that is not where the simplicity to the system ends. This is only where it begins. The data is constantly shifted from source to source, often creating layers and stacks from where they have to be retrieved and show up at the exact time when a related function is called up. This calls for a number of functions including transformation of data, high data quality, monitoring and reconciliation that would allow one to do so without errors, and more than anything else – mitigation of risks and vulnerabilities with data security at the very core of all these diverse operations.
Before we delve any deeper into what goes where and where the data comes from, we need to understand what this data is used for. Data warehouses and analytics are common to most modern day enterprises where each company loads its data from source systems. These source systems could include SAP, Oracle, JD Expert and many others. Now, the thing to remember is that many enterprises would be doing so on a day to day basis while smaller organizations would be doing so on a weekly basis. This is one of the biggest challenges faced by data teams and their internal users as well.
The modern day data stack is something that the modern day data team and data user sees as an impediment rather than a crucial tool for growth. This stack typically takes raw data from the corners that would affect your metrics the most, and then turn them into meaningful bottom lines, neatly arranged and organized in fields for not only visibility and ease of access, but also for the following purposes:
Altering and renegotiating paths and pipelines
When the data is moved many times (which is often the case), a number of tools are used, but in doing so, the data often gets duplicated or deleted entirely. ETL tools like Azure, Data Services, Informatica, 4D Alert and others are often used to reconcile this data. While choosing such a tool, it would be worthwhile to keep data monitoring in mind and choose a tool that offers the same along with data security. Without a tool like this, your enterprise will not be able to reconcile data between the source and the target in an automated way.
Such a tool would also reduce vulnerability to low quality and lapses of security. Also, in an analytics table, there are hundreds of tables, objects and fields which would get loaded on a daily basis – or even more frequently for larger organizations. In this case, the tool would check for outliers on a constant basis to match steps with the speed and bulk of data transfers.
When we talk about data stacks, we often miss out on data monitoring as an important part of the pipeline. By eliminating this step, we are setting ourselves up for a less than organized approach when it comes to setting goals and understanding the path we need to take towards achievement of the same, in terms of resources and time allocation. In doing so, we are essentially missing out on accomplishing our data accuracy, freshness and overall quality.
The data around data monitoring is substantial. The statistics will show what a huge chunk of any industry you are missing out on, when you are not turning to data fueled practices – and more than that, quality data practices that check for the following:
One of the biggest telling signals should be the fact that the data analytics industry itself is a $105 Billion market. Yet, we cannot merely use analytics, we have to monitor and transform this data before it begins to work for us.
This means you would need a very well orchestrated data pipeline built into every process and system of your business, so as to make use of the analytics and data. With this, you would be able to tap into the market segment of your choice even as you define the scale of your business and manage your own growth.
Many data teams and their internal users like Sales VP, supply chain VP and other decision makers, face a gap when it comes to taking data and then making it work for them. This is due to the fact that data might be generic, but what it does for your business or your marketing goals is very specific. In order to bridge this gap, you need to consider a simple plan: data transformation. From extraction to merging and also, keeping your data clean so that there is minimal risk, there are a number of things that we need to ensure before we take the leap into the future of our business or marketing practices. This brings us to the question of how?
Data Integrity. This is one of the most important aspects that we end up overlooking in our quest to use data – any data – in a bid to get ahead. The point is that we cannot merely use any data – we have to use good data, since the cost of bad data is a rather burdensome one with long term repercussions in many cases. Yet, data transformation with monitoring and security are at the very heart of the matter. A data monitoring system would need a data security and data reconciliation process working alongside it, so that we are able to set our best foot forward in customer acquisition practices and audience retention processes as well.
A large scale pipeline with a complex process at the heart of it, does not need to mean that you cannot find the right data or make data work in the right way for you and your business. A data monitoring system is what can save the day. Here’s how.
One of the crux things is to make your data work for you. We are looking at a slope of awareness when it comes to bringing home our audience. Business owners and marketers around the world are concentrating on creating awareness of their solutions and the problems that they can fix with the same. Yet, the point here is to articulate these solutions in the language that the customer understands and speaks. For this, you would need data on how this language works and how it is changing. This is essentially a part of data transformation, which can take the data and turn it around to fit your niche in terms of how you can better articulate what you do and how it can help your end user.
We are now in a generation run by machine learning and analytical skills. While a stack is built and used for years, the data within it needs to be validated constantly. Data needs to be validated in terms of consistency and reliability, above all else. Further, the systems should be able to use this data efficiently in actually driving various functions from production to marketing and much more. This is also crucial when we look at how data can fuel a data driven decision to inch forward on a growth bound journey.
A framework that is immersed in transformation would help your data quality in leaps and bounds. Additionally, it would also help set forth a proper path in terms of the data pipeline, which can go from extracting the data, to cleaning it up, merging it with the right tables and fields for your use, and finally making it work even as there is a system in place to keep it safe and accurate. This should be the integral part of the way the business or the marketing practice works.
This is one of the biggest benefits of data monitoring and data transformation with data integrity at the core. The pipeline, as we discussed, is at the very centre of the entire system. Therefore, it is crucial to run clean data into the workflows in order to avoid overlapping information or generic information that would not fuel your specific goal. This would also give you the scale of the pipeline and help you manage the data in a far better manner. At the end of the day, you would have full visibility into every layer of the data stacks you are dealing with.
The cost of using bad data or even quality data in an inefficient way, is lack of growth. Yet, with data reconciliation as well as data monitoring and a good data security system running parallel to it, you can conduct efficient data transformation for your practices and also ensure that you are on your A game with your data stacks!