Data is no longer the “new oil.”

Though parallels have been drawn between the current data boom and the oil boom of a century ago, data has become a huge opportunity for growth that small businesses need to cash into.

Unlike oil, data is an infinite and all-encompassing ubiquitous concept that has become an invaluable “natural resource.” Therefore, data is no longer the new oil; rather, it’s the lifeblood for the likes of Facebook, Google, and Amazon, which capitalize on 2.5 quintillion bytes of data generated in a day by mobile devices, the internet of things (IoT), and other large data platforms, such as social media.

Over the past decade, larger businesses have drilled down with powerful business intelligence (BI) analytics tools to gain insight from data. They have been quick to adopt new BI technologies such as predictive and prescriptive analytics to get even more insights related to customer behavior, sales, and marketing patterns, outwitting their competition.

So where does that leave small businesses?

Small businesses can take inspiration from their larger counterparts by leveraging BI technologies that are easy to scale, cost-effective, and that address a specific key business challenge, such as customer retention, hiring the right talent, forecasting cash flow management, etc.

However, like many technologies, business intelligence analytics is rapidly transforming. This in itself is a key challenge for small businesses, which must assess the right time frame for technology adoption in both the short and long terms.

Key small business challenge

Advances in BI technology have transformed BI adoption for small businesses, which must now formulate long-term BI strategies in a way they have never been required to before.

Over the next five years, small businesses must create a highly agile BI technology adoption strategy for:

  • Using visual data-discovery BI tools to derive key metrics data from CRM and sales processes
  • Gaining real-time customer insights through personal analytics and conversational analytics platforms

Otherwise, they will be increasingly stagnant compared with their peers with stronger BI adoption strategy models.

In this article, we’re breaking down the three business intelligence technologies that your business needs to adopt in the short and long term. Learn about the “must have,” “mature,” and “transformational” BI tech that will come together to help form your broader BI adoption strategy.

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Infographic based on three critical stages

chart presentation iconVisual data discovery: Replaces statistical jargon with stunning visuals

Visual data discovery (VDD) tools are augmented BI platforms dedicated to presenting interactive data visualization in real time. They combine multiple data sources with an extensive library of visual layouts to customize the data’s presentation.

The primary objective of VDD is to enhance the user’s comprehension and insight of complex data sets by only providing visual data representation without diving deep into the complex reports offered by traditional BI tools.

Why is visual data discovery “must have” BI tech for small businesses?

VDD differs from traditional BI platforms as it focuses on combining analytics with interactive visualization technologies. In contrast, traditional BI platforms process data by first integrating data from multiple data sources, storing the data, and then presenting it visually.

Key business challenges addressed

  • Small business managers may not have a strong IT background and may lack the expertise, training, and time to process complex data sets in reports (which are loaded with statistical jargon). This means they can’t gain quick, critical insights about their business’s key performance indicators (KPIs).
  • Incorrectly interpreting data from traditional BI platforms could misinform critical decision-making around KPIs, such as sales volume, customer leads data, cash flow, and more.
  • Dedicated IT support is required to set up traditional BI platforms, because the process involves integrating BI platforms with data sets and creating an initial query for structured query language (SQL).

Benefits of using visual data discovery tools

  • By presenting data visually, VDD provides immediate insight needed for making critical decisions.
  • Interactive visuals with drag-and-drop interface let users quickly and easily change data parameters instead of coding through SQL.
  • Visuals are updated in real time from multiple sources, saving the time it takes to generate complex reports.

Potential issues to look out for

  • VDD requires training, especially for users who are shifting from a traditional BI model of reporting. However, for users who are new to the concept of BI, the learning curve for VDD won’t be as steep as it would be to learn traditional BI, since VDD platforms are mostly based on graphic visual dashboards with simple drag-and-drop interfaces.
  • VDD tools do not offer all the features of traditional BI platforms, particularly when it comes to access control and manageability.

Recommended actions

  • Adopt this technology now by seeking out software vendors that offer VDD features rather than traditional BI only.
  • Focus your VDD efforts on improving high-volume transactional processes that are captured in your customer relationship management, marketing, sales, and social media analytics platforms.
  • Use VDD to forecast critical business functions, since visual data provides more immediate business insights into areas such as inventory and cash flow levels than traditional reports.
  • Deploy VDD in functional areas where managers and employees don’t have strong IT backgrounds, as these tools provide greater user autonomy for generating business insights compared to report or SQL code.

man iconPersonal analytics: Provides deeper insight into customer preferences

Personal analytics leverages key data points and generates personalized insights, predictions, and recommendations for an individual consumer by analyzing their photos, social interactions, purchases, personal preferences, and more.

Data is captured from interactions with various devices or virtual private assistants (VPAs), e.g., virtual health assistants, financial advice assistants, and shopping assistants.

Why is personal analytics “mature” BI tech for small businesses?

As an emerging analytics function, personal analytics is set to extend the capabilities of BI tools in the next two to five years and become a mainstream component of BI by 2020.

Personal analytics is still in the early stages; however, examples of early adoption can be seen in more customer-focused areas. In the retail industry, for example, the personal information of social media users has been analyzed to customize products and services based on customer preferences.

Key business challenges addressed

  • Identifying key pain points in various stages of the customer journey map based on customer interactions with VPA platforms such as chatbots.
  • Creating a more focused product and services strategy that meets customer expectations.
  • Maintaining and managing the online reputation of your small business and addressing negative feedback or complaints when customers vent during interactions with virtual personal assistants.

Benefits of using personal analytics

  • Improves customer relations by narrowing down potential leads, and increases customer engagement by personalizing the customer experience.
  • Helps to calibrate your customer service according to changing customer preferences.
  • Improves sales and marketing campaigns by better targeting customers.

Potential issues to look out for

  • Integrating personal device platforms that contain sensors and data feeds related to individual users is complex and requires systematic help from IT support.
  • Compliance issues related to sharing personal information of customers to third parties.

Recommended actions

  • Aim to adopt personal analytics as a core component of your small business’s BI capability in the next two to five years, because the evolution of this BI technology depends on how well VPAs integrate with AI to generate more user-specific data for analysis.
  • Test your small business’s readiness with simple personal analytics applications that are easy to use and clearly address specific customer pain points. For example, there are many customer management tools that offer integrations with chatbots.
  • Develop and test personal analytics tools, applications, and APIs with your customers to enhance brand interactions across different sales and marketing touchpoints and channels.
  • Create a CRM strategy that combines personal analytics with natural language processing (NLP) to enhance customer interactions with VPAs. This will, in turn, improve the capabilities of small business sales and marketing departments.

chat bubble iconsConversational analytics: Enhances BI application interfaces through voice and text

Conversational platforms, such as Amazon’s Alexa, Microsoft’s Cortana, and Google Home have changed the way users interact with devices. Similarly, conversational analytics will prove crucial to enhancing user interactions with BI platforms with its increased ease of use.

Why is conversational analytics “transformational” BI tech for small businesses?

Conversational analytics allows users to interact with a personal digital assistant (PDA) or mobile device through voice or text conversations that are powered by NLP and that integrate with BI platforms.

Nascent technology developments such as conversational analytics will eventually render most BI features that are standard today—such as drag-and-drop interfaces—redundant, making way for voice or text-based interface.

Key business challenges addressed

  • Executing data queries through SQL and other databases that are linked to BI platforms often requires a command line interface in which the user must type the necessary SQL instructions to generate insights in any BI tool. With a conversational interface, BI data points are more easily created with voice commands.
  • Compiling key data points in a BI tool by including primary data keys, such as sales figures, to generate a sales report is time-consuming. Conversational analytics addresses this challenge by letting sales managers directly interact with BI chatbots through predefined speech commands such as “generate sales report,” for example.

Benefits of using conversational analytics

  • Empowers any employee in a small business with limited BI analytics knowledge to access analytics content from BI platforms, which have been historically limited to specialists or IT support with advanced analytical and technical skills.
  • Eliminates the need for complex data commands to retrieve detailed BI reports or visualizations and replaces them with just conversing with a BI platform through voice or text-based commands.

Potential issues to look out for

  • Users will require significant upfront training on the voice and text commands used to interact with conversational analytics platforms. Small businesses will need to create a strategy for and have the IT support to implement the technology.
  • Initially, not all BI platforms will integrate conversational analytics, as market adoption will be slow. Moreover, development in NLP technology will be the key success indicator for conversational analytics in the next five to 10 years.

Recommended actions

  • Consider adopting conversational analytics as part of your long-term BI adoption strategy, because conversational analytics won’t see significant vendor adoption in the next five to 10 years, since the technology is still in its early stages of development and is heavily dependent on key developments in NLP.
  • Assess the effectiveness of current conversational technologies that are based on NLP. Based on this assessment, build a roadmap of which key components of your BI platform would be easier to use through conversational analytics.
  • Evaluate the cost benefits and training requirements that your business will need if you want to adopt conversational analytics in the long term.
  • Create a long-term plan for identifying the key BI platform requirements that your small business will need more than five years down the road.

Next steps and additional resources

The technologies mentioned above are just some of the BI tech developments that will transform the BI landscape in the short and long terms. While choosing a BI analytics platform, consider these technologies as you devise a plan for your overall BI development strategies.

Here are some immediate steps for business intelligence adoption strategies that you should consider:

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Check out the following GetApp resources related to BI to streamline your business intelligence adoption strategy:


The progression for adopting key BI technologies is based on three critical stages:

  • “Must have” technology is a widely used technology that small businesses must consider adopting for critical business functions
  • “Mature technology” is offered by many vendors, and small businesses can look at implementing them in the short-to-medium term.
  • “Transformational technology” is still in its nascent stages and can offer small businesses the best returns in the medium-to-long run.