3 Ways to Face Complex Data With Confidence

“Big data” has become such a powerful trend among businesses that at least 75% of all companies are planning to have invested in data initiatives within the next two years. Not too long ago, only big corporations had the means to work with powerful data science platforms. In the past two years, however, the availability of self-service and cloud-based business intelligence (BI) tools has made it possible for startups and SMEs to make better data-driven decisions that can enhance operations, improve sales and accelerate growth.

Data is becoming bigger and more complex, however, and businesses need to develop strategies for keeping up. According to a 2015 paper published by Aberdeen Group, 93% of organizations saw significant growth in data asset size over the course of the previous year. With the increasing size and complexity of data, it gets harder to maintain visibility and insights needed for effective BI.

Even the buzz term “big data” is no longer as applicable as is once was, given the increasing complexities of the raw materials. The more accurate term now is “complex data,” since BI specialists are now working with growing data sets, information sources and relationships. However, you need the tools and know-how in order to better harness the power of complex data. When leveraged correctly, this data can be used to refine your business decision-making skills. Here’s how to get started.

  1. Keep it Simple

With larger data sets, increasingly diverse sources and greater data volumes, you can extract stronger insights for making sound business decisions with reduced lag times. The challenge is to profile and integrate disparate data with the right IT resources.

Remember that the key to making complex data useful is to establish an approach that simplifies the work. In the past, effective BI initiatives required input from data scientists who analyzed complex data and prepared visual reports that could be understood by everyone. Not only is it time-consuming, but this also requires extensive training in data analysis and management applications.

Modern BI solutions aim to simplify all of this through various means, thus enabling SMEs to streamline and automate essential processes from compilation to reporting. One leading data analytics platform called Sisense simplifies the way businesses handle complex data analytics with an easy drag-and-drop interface and customizable libraries of visualizations. By optimizing its analytics for in-memory and in-chip processing, the platform also lets businesses manage their analytics using office-grade desktop computers instead of expensive servers and workstations, significantly reducing the technical barriers required to crunch the data.

The key benefit of simplicity is agility. Since you will no longer need to spend weeks or even months in design, data preparation, programming and other work, you can simply act on the decision-points on an operational basis.

  1. Keep Costs Down

Enterprise BI was once prohibitively expensive, as it required dedicated hardware and software that significantly increased the cost of deployment – not to mention the maintenance and consultancy costs that businesses needed to pay data science professionals. Today, you can significantly reduce costs through self-service analytics.

Self-service BI platforms today offer scalable solutions that can fit the needs of businesses regardless of size and industry. As a business, you should take advantage of this by looking for providers that offer flexible volume-based or process-based pricing.

Aside from eliminating the need to acquire expensive hardware, self-service BI tools also remove the need for data experts and consultants, which can save you even more money in the long run. This is perfect not only for startups and SMEs with limited budgets, but also enterprises with wider and more complex needs.

Cloud-based BI tools are increasingly becoming popular, especially among businesses that do not want to invest on their own infrastructure. Some examples include IBM’s Watson Analytics and Microsoft’s PowerBI, both of which can be offered as standalone software-as-a-service solutions or as part of these providers’ respective cloud-driven enterprise suites.

Self-service BI’s scalability is further enhanced by the availability of free data sources such as content on government websites like Data.gov, as well as social media platforms. In fact, insights from social media can be goldmines for businesses seeking to tap into targeted niche audiences and build out optimized products. Self-service BI platforms will usually let you integrate social media as an external data source using a service like RSSBus. Other ways to save time and data costs include using sources such as Google Analytics, Google AdWords and custom SQL servers.

  1. Make Sure It’s Accessible

By eliminating the need for extensive training, additional infrastructure (hardware and software) and external consultants, BI is now more accessible than ever. For example, Sisense optimizes the use of in-chip and in-memory analytics, which maximizes the processing power of most modern desktops. This means that you no don’t even need to acquire server-grade computers or subscribe to virtualized infrastructure in order to gain effective BI.

In addition, your solutions provider should seamlessly interface with other platforms like finance, CRM or ERP applications like Due.com, Salesforce and Zendesk for more effective use of data.

Apart from being accessible, complex data should also be actionable. With simplified integrations, you can ensure total visibility over your data and your business as a whole.

  • Over to You

Whether you are an established business with terabytes of data to process or a startup/SME that simply needs to make better data-driven decisions, data science effectiveness is a challenge.

To make sure you’re on the right path towards useful insights with minimal lag times, make sure to simplify your BI initiatives, use a cost-effective solution, and maximize the accessibility of your insights across all departments.

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