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The business world has never been more dependent on business intelligence capabilities. The rise in recruitment of data analysts in the US and the recent call in Australia to “teach kids to code in schools” indicate an understanding that the future workforce will need to harness the magic of data and make sense of it.
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One of the challenges for business leaders who will continue to need to leverage internal information to analyze performance is the ever-increasing need for testing; scenarios ‘what if?’, ‘what if?’ and ‘what’s out there?’.
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As global online business and consumer-driven markets for goods and services grow, relevant external information and trend analysis are critical to continued business competitiveness.
The challenge for all businesses is that disruption, including machine learning, artificial intelligence and big data analytics capabilities, is the new norm. Businesses can expect the need to access and use intelligence to continue to expand over the next five years. Fortunately, new online tools and specialized BI startups are filling the current capacity and capability gap in data analytics.
The purpose of this article is to support the business leader’s understanding of the role of the business intelligence analyst to enable their effective contribution to the organization.
If you are looking to establish a Business Intelligence Analyst role, this article also outlines some of the internal reasons for appointing a BIA and the expectations for the range of skills that may be required to best address business needs.
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For those looking to contribute to a business in a Business Intelligence Analyst role, this article will provide some insight into where the role intersects in the business environment and what capabilities you can bring to the role.
The purpose of the role as a key factor will be to support business competitiveness while identifying new opportunities for the company’s progress.
As any good intelligence analyst would do, I asked fellow BI enthusiast Chris Ong, Head of BI and Development at the University of Newcastle, to prepare this article.
My experience includes qualitative information and knowledge management with a background in geographic information systems and disease management systems in government. Chris describes his role as a problem solver: process, data and people and has industry and global business experience with BHP Billiton.
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We now have a common interest in intelligence management in higher education from both an educational market and a research development perspective.
Business intelligence (BI) can be described as sets of information provided through data analysis and knowledge management that can inform decision makers about areas for response. This may be related to emerging external trends or changing internal performance requirements.
A simple way to think about BI is to look at whether the data that defines business development goals is drawn from internal systems or external sources.
Internal information in large organizations will generally come from enterprise business systems such as SAP or ORACLE database systems, or may be drawn from a range of more distributed and often informal data capture systems across the organization.
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This data can then be analyzed for BAU reporting and tested against other information to determine how the organization is performing against expectations. External sources can be directly business-oriented, such as market research to provide information about customer expectations or more formal analysis to identify what trends are “out there” that the business may need to respond to.
External information would also provide information about the competitive profile of similar companies. Increasingly, companies can draw on external “self-service” web analytics resources when responding to external changes.
In marketing, capturing social media trends through Google Analytics, Kissmetrics or similar analytics tools is now a familiar activity, but understanding the set of information may not always be straightforward. When describing the business intelligence framework in an organization, Chris describes his view with the analogy of an onion.
Like any component of a business, the broader business intelligence system must deliver value, and the cost of adopting and adapting large enterprise data systems is being challenged by the lower costs and more agile options of cloud and web services.
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In many large organizations, both forms of demand for external and internal information intelligence are coming together with the Data Lake concept.
This assumes formal data warehouse designs where the inputs are typically highly structured and the outputs are packaged for a standard reporting level. A data lake means that data can come from multiple sources and be “aggregated” in a less structured form that can be “exploited” as needed with new analytical tools or programmed queries.
Open source packages such as Apache’s Hadoop, which is used by major web services such as Amazon, Twitter, and eBay, are an example of a software tool that can be used to draw data from a set of data storage clusters.
This information is used for data processing and to solve new business issues. Martin Fowler describes Data Lake simply as a store for
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Data, regardless of the format provided by the data source. There are no assumptions about the data schema, any data source can use any schema. Consumers of this data must understand this data for their own purposes.
As shown in the chart below, developed by Tamara Dull, Director of Emerging Technologies, SAS Best Practices, the growth of the Data Lake concept is also fueling the demand for data scientist skills that empower business intelligence analysts more than other business professionals.
While Martin suggests in his diagram that “we” choose the data for each need, in reality selecting, extracting and analyzing the “right” data from the data lake is still the domain of the specialist data scientist or analyst.
The table is from Dull (2015). While the “one-size-fits-all data system” that dominates the enterprise stack of large organizations is rapidly changing to more agile approaches, data security and statistical validity challenges remain for Data Lake users. A structured data form is likely to continue to be considered essential for business-critical areas.
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As information management and information systems become less rigid, the role of a business intelligence analyst requires a degree of agility to inform and respond to changing business needs.
The primary role of a business intelligence analyst in an organization’s overall business intelligence system is to provide a vertical bridge across the enterprise to communicate high-value information in support of decision-making requirements. If the BIA role takes a “business insight” approach and the analyst is enabled to work with decision makers, information managers and external service providers to continuously engage with business development, they are more likely to deliver value. Often the first step for an incoming BIA is orientation and understanding of the current state of business.
BI must be able to take into account the organization’s existing systems and company culture. They will need to understand, “What are business development goals? What systems and mechanisms for capturing, storing and processing data are used? What methods are used for analysis, i.e. how automated are the systems? How creative or mechanical are the requirements?”
A business intelligence analyst can cover different parts of an organization, often working from a more internal or external perspective. In large organizations, you would expect BIA to bring a certain level of expertise in dealing with big data and to inform future intelligence gathering priorities.
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As a first step, BIA can be expected to work across the organization to provide an assessment of the state of the “4 ‘V’s of Big Data”, a term coined by IBM to provide a framework that can inform business leadership:
While the types of skills required will depend heavily on the size and scope of the organization, the BIA can be expected to add value from the outset with their technical, analytical and problem-solving skills. They will often come with backgrounds and qualifications in information technology, data science and computer engineering.
Despite this background, the role must be clearly differentiated from an IT or engineering professional who may be expected to perform analysis to produce a business technology solution for the organization.
Rather than providing specific technical solutions, BIAs bring an understanding of data collection and analytics to building intelligence and knowledge. In any organization, this can be a broad activity that can include:
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For BIA roles focused on business marketing, the organization would expect knowledge and experience in a wide range of social media platforms and media analytics tools.
A dual degree in Information Technology and a Communication and Media qualification would be an advantage for these positions. However, if the organization envisages an expansion of the role in the future or requires a BIA who is ready to move into a senior or management role, the job description should normally outline the expectations for wider business knowledge, communication expertise and potentially relevant experience in the sector. .
Successful candidates will normally be expected to have postgraduate qualifications such as
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