Data storage and analytical solutions are now available at more SME-friendly prices with the emergence of web-based technology. We discuss the pros and cons for SMEs to consider adopting the technology without breaking the bank.
For a long time, big data has been associated with large businesses, such as tech giants like Tencent Holdings or Alibaba. These industry giants have pockets deep enough to invest in a team of highly specialized data experts to sort through colossal amounts of information they have gathered.
But what about the rest of the players—the small and medium enterprises (SMEs) with limited resources and budgets? While 53 percent of all businesses globally use data analytics to drive their business, SMEs who don’t leverage this technology will be left further behind the digital ladder amid ongoing disruption.
New technologies and AI, though, are changing the game by enabling the development of big data solutions that are scalable and affordable, allowing SMEs to pay only for the features and the specific number of licences needed. These solutions also come with user-friendly interfaces, lowering the learning curve necessary for grasping big data insights.
With these opportunities at hand, it’s now just a matter of figuring out which data analytics solution best suits your business.
Data analytics for SMEs
The key to getting the best bang out of your big data budget is to get a solution that fits your organisation’s specific needs. Here are a couple of ways you can get started quickly.
1. Evaluate your data sources
The first thing you need to do is evaluate your various data channels and assess their lifecycles. Data can come from numerous sources including applications, sales, your website, social media, digital advertising tools, review aggregators, and more. Ask yourself: Where is your data coming from? How much data do you currently have?
Most importantly, which data is vital for you to improve business processes? It’s crucial to identify the metrics and data-related questions that can support your decision making. From there, build your big data strategy.
2. Are you already tracking your data?
If so, you should be taking a closer look at the data that you already possess. Through the use of self-service tools, SMEs can collect information over longer periods of time, even without an integrated big data system. For example, by tracking historical data, you may start noticing insights and trends that can be used to help shape future business decisions and strategy.
3. Conduct data science experiments
If you’re still unsure about how to integrate data analytics into your business, try running a controlled experiment with clearly defined perimeters.
For example, if you want to use data analytics to improve your mobile application’s customer experience, roll out a new feature to a select group of users, analyze how the new feature affects app usage, and compare the results with the group that didn’t receive it.
If this experiment proves useful, you now have proof that your business would benefit from data analytics and a use case that justifies getting a SaaS or customized PaaS data solution.
*Read more about SaaS and PaaS in point 5 below
You can also conduct feasibility studies or A/B tests with the current data sets to evaluate if there are any meaningful insights that can be extracted. Try something as simple as changing the approach to your messaging, revising the tonality of a copy or perhaps, even the styling for images used. You can run both the old and new versions and compare the outcomes. The results of your experiment might surprise you.
4. Scour the internet for free software
There are several free, open-source software that can be used for big data analysis. It does require quite a bit of research, time and technical skill to get them to work properly, not to mention the steep learning curve expected and documentation to mull over.
Hadoop is one of the first free, open-source big data frameworks and it’s still a popular choice for big data solutions. The internet has since been populated with other useful open source tools, including Hadoop’s successors, Spark, Storm, and Cassandra.
5. CONsider adopting a SaaS/PaaS Solution
Looking for business intelligence software can be a daunting task to the uninitiated. Each solution has its own benefits as well variances and it is necessary to understand the differences to know how to best select one for your organization. Here is an explanation of what SaaS and PaaS solutions are and examples of when they can be used.
Saas (On-Demand Business Intelligence): Software as a Service
Software as a Service represents the most commonly utilized option for businesses in the cloud market. SaaS utilizes the internet to deliver applications to its users, which are managed by a third-party vendor. A majority of SaaS applications are run directly through the web browser, and do not require any downloads or installations on the client side.
SaaS provides numerous advantages to employees and companies by greatly reducing the time and money spent on tedious tasks such as installing, managing, and upgrading software. This frees up a lot of time for staff to spend on more pressing matters and issues within the organization.
SaaS data analytics solutions can help users extract, transform, and integrate data from different sources, as well as analyze and visualize the information. These ready-made data analytics solutions are convenient for small businesses because the SaaS provider takes care of building the infrastructure and managing administrative and technical tasks. All SMEs need to do is type, click, drag and drop.
SaaS solutions are great for organizations that don’t have unique data integration requirements. They also offer different price points depending on the features you need, and charge based on the number of users who will access the software. The drawback for most SaaS solutions, though, is the lack of flexibility to adapt the software to a specific user’s needs compared to, say, a customized analytics pipeline or industry vertical. If you don’t have highly specific data sets or specialized use cases, a SaaS solution may work.
With a user-friendly SaaS solution, you can forego the hiring of a data scientist and work with an analyst instead. In many cases, it will be easy for even non-tech team members to learn how to use the software, too.
PaaS: platform as a service
Cloud platform services, or Platform as a Service (PaaS), provide cloud components to certain software while being used mainly for applications. PaaS provides a framework for developers to build upon and use to create customized applications. All servers, storage, and networking can be managed by the enterprise or a third-party provider while the developers can maintain management of the applications.
There are numerous advantages for using PaaS but the main two benefits are reduced costs and increased speed of development and deployment. Best of all, every one of these advantages can be acquired for a fee that is affordable to small and large businesses alike, leveling the playing field of software development and empowering small to medium size businesses to create software solutions, websites, and applications that are of the highest possible quality.
Popular paas Examples
Google App Engine, Salesforce Heroku, Microsoft Azure, Amazon Web Services
PaaS solutions allow customizing of the analytics suite without having to build the pipeline from scratch. Simply put, PaaS delivers on a platform on which people can develop software—but with the operating system, server software and hardware, and network infrastructure already provided. This solution might work especially well for SMEs who find that SaaS lacking in features or doesn’t work as intuitively as they’d like for it to.
6. Hiring in-house specialists
Data scientists are experts in big data. They can make sense of vast amounts of information, ask the right questions, and glean actionable insights that can help organizations increase accuracy, develop data-driven strategies, and improve operational efficiencies. These guys have specialized skill sets, which make them scarce and highly in demand.
This niche, however, comes at a price. A study by Indeed.com revealed that data scientists with big data skills command a $123,000 annual salary, which is nearly double that of a regular data analyst or business analyst. They also require huge amounts of data to work with in order for their skills to be fully utilized.
This means businesses that are in the early stages of their analytics initiatives may not have enough data to keep a data scientist busy, which would make it difficult to justify the cost of employing one—assuming they have the budget to do so in the first place.
Big data for a better perspective on business
Big data, when harnessed effectively, can help business leaders get valuable insights, analyze problems, make effective decisions, predict trends, and develop better products and services. Bear in mind that the more expensive solution doesn’t necessarily guarantee a higher return. It’s more about choosing the solution that best fits your business needs and resources.
Thanks to the rise of scalable analytics platforms, companies with small budgets can now glean insights from large volumes of data. These days, when it comes to data analytics, even the small guys can reap big success.
The Forefront of Data Analytics — AI Builders of the Future
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