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The internet has made it easy for people to access, use and manage a vast wealth of information while leaving digital footprints, which can also be accessed and used independently. The ever-growing world of Big Data and Business Intelligence has, over the last decade, transformed every facet of business, way of life and decision-making in an unprecedented way.
Businesses now have access to exceptionally large volumes of information which can be used to plan, strategize and take action. Consequently, there has also been a concerted effort to build data-based applications and products, in order to enable better use of this important resource. There are a lot of advantages to be had from the custom-building of data layered products, both from the standpoint of a business and its customers.
The avenues of potential opportunities are endless, and businesses all over the world are scurrying to make the most of the Big Data revolution every possible way they can. With the companies spending millions on data layered products, it is not all a cakewalk. There are veritable challenges in the world of creating data layer product creation that these companies have to face up to.
Investing into large chunks of data does not end up being a rewarding, cost-effective venture unless the data quality is high, actionable nature and can be effectively used as a resource which helps in business-wide decision-making. The actual amount of information needed to empower the immense product data feeds, and the associated product data extraction exercise, are gargantuan to begin with.
There is also the problem of keeping things relevant and to-the-point when it comes to the use of data layers in the final product. Let us take a closer look at some of the usual roadblocks that companies might experience while building data layered products.
What is a Data Layered Product?
A data layer can loosely be defined as a place or structured storage area for all the data you want your application to process, manage and make available either to the user, or to other linked products.
The whole point to the efficacy of the data layer is in those situations where it is necessary to separately store and access semantic information from other kinds of information. If you use readily available information to bolster your product, any possible change in the source has the potential to compromise your data integrity. The use of a data layer subverts that possibility and keeps things together.
Nuances and Importance of Data Layered Products
For those who build data layered products, it is important to harvest, standardize, store, access, manage and monitor data from multiple information sources. This structured, relevant data is what then powers the information engine in your product.
The retrieval, storage, categorization and access of this information presents certain palpable roadblocks that you are likely to face at some point of time or the other during your endeavour to build a data layered product. There is a large variety of different techniques and tools involved, from databases to advanced web scraping and data mining tools.
Without the right kind of foundation and organized resources, getting together a productive, efficient data layer is a difficult proposition. Some of these roadblocks can be taken care of relatively easily, while others take considerable planning and action to resolve.
1. Scaling of Data
The internet is already a massive, throbbing source of information and is still growing exponentially. For your product, you might need a certain amount of data to start off with. However, over time, with new information being added daily and your product being deployed for use in various industries, the need for more information is extremely likely to arise.
This entails that you need the ability to constantly scale up the volume of resources that you monitor for the information that makes up your data layer. Having a system which is scalable, while remaining easy and intuitive, is one of the principal roadblocks. The best solution to this problem is being able to construct a system of data retrieval which is intelligent in and of itself.
For instance, if you are creating a product catalogue using collected data, your collection should intelligently be able to gather only the relevant amount of data from every product page. The kind of web extraction you would look at would involve the platform mimicking human behaviour while extracting important information, making it easier to scale. This way, productivity can get better over time while the costs remain in control due to minimal wastage of effort.
2. Data Acquisition
Web sources of information are changing as rapidly as the technology they use. Most of the more archaic data acquisition methods no longer work, owing to their inability to adjust to modern technological innovations that abound on the internet. One commonly experienced roadblock is when product creators find their usual sources of information no longer supporting their methods of data acquisition.
The way forward is to invest in information harvesting platforms which are built with the latest technologies already in mind. You need a platform which can access, analyze and accurately mine modern, dynamic websites without putting too much stress on resources and bandwidth.
3. Data Processing
With the collection of large amounts of data for the data layer in your product, it can be expected that some kind of minimal processing and standardization of data will be required for it to be usable in your particular context. With time, as you collect more and more data from more sources and keep aggregating them to a central location in one standardized format, the amount of data processing might keep climbing to unreasonable levels.
You might find the processing alone taking up a large chunk of the time and financial resources that you have for the project. The trick here is to opt for a collection platform that does most of the filtering, structuring and standardizing at the source point rather than in your system.
If the platform itself can maintain some degree of order while collecting the data, and can impart some minimal processing and structuring to it before sending it for storage in your system, the demands on your resources decrease dramatically and you are left with more time to devote to other, more pressing areas of your work.
4. Data Efficiency
When you are starting your drive to collect information to power your data layer, you need large volumes of information which might require you to extract all or most of the data from every resource location. However, you are sure to reach a point of time when you already have a minimum amount of data at your disposal, and only need to monitor these resource locations for changes and updates.
Keeping a large-scale collection platform up and running at this point is counter-intuitive, as you have to needlessly keep mining information you already have. This can slow things down and create a loop of inefficiency to your system.
The solution is to get a data collection platform that is sensitive to change and can proactively fine-tune its data collection with regard to every single resource location according to the amount of information you already have. This way, you can take out most of the labor of the effort, while being able to accurately reflect any change or update to the data almost in real-time, preserving data integrity and freshness.
5. Data Utility
Many of the companies that create data layered products eventually have to come face to face with this crucial roadblock–are their products providing enough utility? Creating a data layered product for the sake of it is never a wise move, considering the large amount of time, resources and effort that is required during the creation process.
A data layered product that just addresses a particular kind of problem might be successful for a while, but for long-term utility and sustained business, you need to be able to provide people with a product that can help achieve long-term goals and affect long-term decisions.
The primary objective of a data layered product must always be on the quality of information and the total utility it provides in the long term. The focus must firmly be on meeting a particular set of needs and using structured, relevant data to facilitate that.
The success or failure of data layered products finally hinges on how well the creators can deal with these roadblocks.
With high-quality data layered products, there is immense potential in a number of domains of business, and the right way to leverage the power of big data is through the creation of products that are easy to use, intuitive, high-performance, flexible, scalable and versatile, being able to adapt to new situations and still retaining their power to produce the required results. With products like these, you can truly give your business the best way of deriving rich rewards from the enticing world of Big Data.
