If you are a small business in a growth phase, or you want to ramp up growth to achieve your objectives, data is going to be crucial. With big data technologies and solutions, you can get a better understanding of your customers as well as trends in the market. You can keep a better check on your competitors while improving productivity and efficiency in your operations, and you can become better at identifying both risks and opportunities.
What are the main big data technologies that can deliver these benefits and accelerate growth in your business? Here are some of the most important.
There are four main types of data analytics that are important to small businesses, particularly in scale-up phases. They are:
- Descriptive Analytics
- Diagnostic Analytics
- Predictive Analytics
- Prescriptive Analytics
Descriptive analytics looks at what has happened in the past, so includes things like revenue reports and inventory reports. Diagnostic analytics looks at why the past happened. So, for example, while descriptive analytics will give you information on sales for the past quarter, diagnostic analytics will tell you why sales were up or down over a certain period – i.e., because of marketing activities, the introduction of a new product line, etc.
Descriptive and diagnostic analytics are being improved by technologies, and they are a crucial part of running a successful small business, particularly as you begin to scale. However, the really exciting areas of the data analytics field for SMEs and start-ups are predictive analytics and prescriptive analytics.
Predictive analytics is about predicting future outcomes and looking at what is likely to happen in the future. Sales and inventory forecasting are simple examples, but the level of drill down you can achieve is significant.
For example, depending on your industry, you could use predictive analytics to discover the days of the year that customers are most likely to buy your products, allowing you to tailor your marketing strategy accordingly. Another example is predicting how internal and external events will impact your supply chain, helping you increase resiliency.
Prescriptive analytics is the real cutting edge of data analytics technologies. The start-ups and SMEs that can harness the power of prescriptive analytics are the companies that will be the biggest success stories of the future. What is prescriptive analytics, though?
Like predictive analytics, prescriptive analytics predicts what is going to happen in the future. It doesn’t stop there, though, as it then uses machine learning, computer vision, natural language processing, and a range of other advanced technologies to simulate multiple scenarios to assess their impact on the predicted outcome.
Prescriptive analytics then makes suggestions on the actions you should take to optimize the position of your business. In other words, prescriptive analytics helps you achieve true data-driven decision-making.
To make use of data, small businesses need data storage solutions. As you ramp up operations to achieve your growth goals, the complexity and size of your data sets will increase.
There are two main types of data storage technology that are relevant to forward-thinking, data-driven SMEs:
- Data lakes – data lakes are used to store all types of data, including both structured and unstructured data. In fact, one of the main distinguishing factors of a data lake is the data it stores is not organized. Data lake technologies allow you to store all your data in one place, whatever form it takes. This makes it easier to analyze and use the data, including for analytics purposes, without transforming it into structured data.
- Data warehouses – data warehouses are also used for storing data, but they are more structured to improve analysis. As a result, data warehouses are used for reporting, regulatory compliance, and a number of other essential business purposes.
It is also worth mentioning NoSQL databases at this point. The term NoSQL database refers to non-relational database technologies. This is a highly technical area, but NoSQL databases basically allow the storing of massive data sets and data that is unstructured. This makes it easier for developers to build the big data solutions your company needs.
The other benefits of NoSQL databases include fast query speeds, horizontal scaling (which helps to control the cost of data storage), and flexible data models that allow your business to become more agile and adapt more quickly to market conditions, customer requirements, etc.
Data visualization technologies present data in a way that makes it easier to understand what the data is saying and to find key insights. Practical examples of data visualization include charts and graphs, but it can also include maps, timelines, tables, and more.
The most important benefit of data visualization technologies is they allow you to unlock the potential of big data. Without good data visualization, all you have is lots of data. Making sense of this information manually would be very costly and time-consuming, plus errors would be common. Data visualization tools automate the process of making sense of the data in your business.
With the understanding that data visualization provides, you can make better and faster decisions, you’ll have better oversight of your business, and you can quickly identify risks, errors, and threats.
AI and Machine Learning
In the past, AI and machine learning would have been viewed as enterprise solutions, but this is no longer the case. More and more forward-thinking small businesses are developing and implementing machine learning solutions as the technologies become more affordable and accessible.
This includes solutions to accelerate the use of data science. For example, machine learning algorithms can be used to collect and analyze data before making decisions, both supervised and unsupervised decisions.
With machine learning, your company will be able to more effectively extract value from the data you collect, as machine learning can do things like identify patterns in large and complex data sets that would otherwise go undetected.
Edge computing technologies enable the processing of data at the edge. It is a rapidly growing field, and it has particular relevance to big data. One of the main benefits of edge computing is real-time analytics, but there is not always a need to store in the cloud all the data that is produced. Edge computing makes it possible to filter data at the edge, sending only relevant data to the cloud for further storage and processing.
Big Data and Small Businesses
With the AI platforms, tools, and solutions that are available, even the most advanced technologies are now accessible to forward-thinking small businesses. Big data makes the difficult and impossible possible, and it can help accelerate growth in your business, whether a startup, scale-up or an SME.