When it comes to software development in Sri Lanka, a customer-centric approach has long since been the norm for a multitude of reasons. Software development and the cloud go hand-in-hand nowadays, that too. So whether you’ve got an existing cloud offering or are allowing your IT agency to offer it (especially if they are a dedicated AWS partner, for example), you will need to address the same key issues for long-term success. Establishing a sustainable software development lifecycle is already a wondrous feat in itself, especially if its offshored halfway round the world. As both entities (client and developer) get accustomed to an efficient work process that’s ideal for ongoing DevOps, is there anything else beyond this that needs to also be considered?
You and your developer may have instilled a productive software development lifecycle, but what happens post-deployment is a whole new aspect of not just making sure that your application is constantly monitored and maintained – but its many outputs are controlled appropriately. What does this mean? In layman’s terms, a well-functioning digital application is bound to gather data with every visit, click, flip, tap and swipe. Of course, this is inevitable, so have you thought about what you are going to do to capture this data in a resourceful yet secure manner?
Today’s fast-paced and interconnected digital landscape amasses more data per interaction than ever before – and it’s not unreasonable to assume that this frequency and quantity will only increase with time. Before you know it, your application may be saturating your databases. This is even true for the most elementary digital applications – so no digital presence is immune to this.
However, data is infinitely valuable today – which makes this influx a boom for businesses of every shape and size. If observed and dissected correctly, all this otherwise raw data can pave the way for impactful decision-making, powerful product development, benchmarked customer satisfaction, and anything else in between. What may initially come across as overwhelming is in fact a precious asset, which gives every business complete insight towards the potential that lies beneath the abyss – if only they know how and where to look.
Before you embark on the journey of sourcing, storing, identifying and examining your data, it’s useful to begin with the knowledge of a few basics that are relevant to this topic. Enter big data, data science and data analytics – three terms which are frequently used in the data-centric fields of expertise, but are often used interchangeably as if they mean the same thing. Here, we’ll focus on disentangling all three nuances, so a clearer understanding of the definitions behind each can be provided, and how they complement one another in the field of data.
Comprising of raw data that is sourced from various digital applications a business/brand may be operating, big data can come in structured or unstructured form. Whether it’s a happenstance website visit or a regular monthly transaction, every interaction counts, making data exponentially add up over time. No matter how you wish to crunch and churn for resourceful insights, big data is always the starting point. Think of it as the base ingredient to a complex dish – without it, there is no outcome.
Then again, another question is formed, when it comes to terminology. Why not call this influx of figures as just ‘data’? Isn’t that what it ultimately is? What really differentiates big data from its conventional data counterpart is when you notice two things happening in your back-end systems. For one, your existing databases are quickly running out of space, even if they’re hosted via scalable cloud support services. Secondly, your existing data analytics tools (which could be basic functionalities that are included with any SaaS subscription) aren’t sufficient to tackle vast amounts of data both in terms of quantity and insight level.
It is at this point that you can safely assume the fact that what you are dealing with is big data, by its textbook definition. You can only do so much to glean rudimental insights, so a stringent Business Intelligence (BI) platform is now a must. Data and the various forms of making better sense of it all is a topic that’s constantly abuzz with discussion. But many businesses are still in the dark about how to reap the vast amounts of potential that lie within their data repositories – which brings us to our next point.
Utilizing mathematical, statistical, programmatic and even predictive modelling techniques, data science aims to understand trends and correlations between existing sets of data. As mentioned above, all areas of studying data require big data as the source, and data science is no exception. Data science has been particularly resourceful for machine learning, as it enables AI to ‘learn’ from what already exists in order to offer improved recommendations and even product upgrades.
Another area where data science has also been heavily applied to is search engines, where previous trends are monitored to offer the most relevant results. More specifically, this is done via Deep Learning, which is a subset of machine learning that forms conclusions based on non-linear (or web-like) processing. In other words, the output of the preceding step determines the output of the succeeding step, and so on and so forth. All in all, this is a massive step up from otherwise static search engine algorithms, because neither are they able to ‘learn’ on their own, and nor can they be updated unless manual intervention is made.
In a nutshell, data science looks from the outside and in, to identify trends, patterns or correlations amidst vast data sets previously not known or factored for by businesses. By observing what the data shows, businesses can re-align their priorities and strategies to suit any shifts – something that disruptive companies are already doing day in and day out. While this may be the secret sauce for businesses that are global leaders in what they specialize in, it’s a wake-up call for those that are still inert – as a lack of action can be costly in today’s highly competitive business arena.
Focusing on answering questions and solving problems, data analytics aims to deliver insights based on what one already has in mind. Contrary to data science, data analytics perceives from the inside and out; when business owners ask tough questions, data analytics focuses on obtaining, processing and delivering just the right data in response. In essence, data analytics comes under data science, with the latter overarching the former. Modern BI solutions are mainly used for comprehensive data analytics, as existing data sets can be used to display results based on parameters and filters that are customized to suit one’s requirements.
On the other hand, data science via forecasting/predictive modelling can also be supplied by the same BI platform, as today’s solutions offer unified capabilities that can also be integrated with external solutions, for leveraging overall use. Having become mainstream, data analytics has paved the way for many a business to ask important questions, while waiting for answers with little downtime. Unlike the BI solutions of yesteryear, modern platforms are also easy to configure, thereby requiring minimal intervention from IT staff. Subject matter experts can directly interact with the system, to engage and reveal insights that can be supplied on an on-demand basis.
While big data comprises of raw data collected upon each interaction, both data science and data analytics focus on making better sense of big data.
Data science looks from the ‘outside and in’, by adopting mathematical, statistical and/or programming knowledge to identify patterns in big data.
But data analytics looks from the ’inside and out’, by using existing data sets to answer tough business questions for strategic decision making.
In the wake of collecting big data and making sense of it all through data science and/or data analytics, many fail to notice the true origins of their data. Getting answers to complex business questions or administering intricate mathematical equations becomes so prioritized that data sources are seldom checked for accuracy and relevance. As much as the right strategy is imperative to produce precise insights, so is the integrity of your raw data. Without accurate raw data, analytics strategies (no matter how advanced) are futile, for no accurate insights can be achieved as a result.
Asking where your data is coming from is a good place to start. But then again, this is expansive, and needs to be broken down into smaller parts that can be addressed more constructively. So here are a few icebreaker questions you can ask your team to encourage a candid discussion:
– What touchpoints are available between the business and its many stakeholders? How many are there? Make a list if you like, to understand the scope better.
– Are all these touchpoints secure, with only authorized individuals being allowed access?
– Is inputted data being validated to check for inconsistencies? If so, how?
In essence, all your data needs to be arriving directly from your business/brand touchpoints, be they online or offline. Any infiltration from external sources that are invalid need to be immediately identified – and administering competent validation checks is essential for this purpose. Even a single data item (no matter how minute) can easily camouflage itself amidst a sea of numbers – but can wreak havoc in due course of crunching those very numbers. This can obviously have negative impacts towards your business; inaccurate data leads to inaccurate feedback, which results in incorrect decision making. From operational setbacks to budget depletion, the results you desire will be hard to see. On the outset, it may either seem like your analytics strategies are unproductive, or your entire BI solution is faulty.
Inversely, noticing a disparity of this sort between your data-backed decisions and final outcomes is also a symptom of poor data integrity. Look within by asking the same questions as stated above, but with extra perceptiveness as these disparities have already been experienced hands-on. This way, you can circle out the problem area(s) depending on your outcomes, for a remedy that is faster and better targeted.
In a world that is highly digitized, consumer-first and on-demand, staying one step ahead of your competition is necessary to sustain high business performance. A winning strategy is made up of numerous important nuances, both big and small. From having an iterative digital presence to understanding your customers deeply, everything plays an equally big hand – and also complements other nuances in turn.
One such aspect is data. But just data alone isn’t going to cut it. Why? Since raw bits and pieces of data can only take your team so far to analyse what lies within, you need tools and strategies that are powerful yet precise. With data, understand everything from operational glitches to customer desires, so that you can tweak your business ideas for enriching processes across the board.
Big data, data science and data analytics are all important components to the field of studying data. Although they overlap with one another, each still bears a unique definition. Big data can be defined as raw data sets that are obtained from a multitude of digital products. Data science is the manner in which trends and patterns can be sought from all that raw data, by applying mathematical, statistical and programmatic equations to it. On the other hand, data analytics can be classified as an avenue to answer tough business quandaries, by revolving strategies to obtain, process and deliver insights around those very business problems.
Although advanced methodologies for understanding data are crucial for intelligent decision making, it all needs to start with accurate and genuine data. In other words, the integrity of your data i.e. where it comes from, how and from whom is essential to build a business data strategy that’s truly reliable.
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