Ian Bertram – iStart keeping business informed on technology

Gartner untangles Analytics: BI, BA & big data

Jennene Kelly — Mon, 15 Sep 2014 22:33:16 +0000

BI or BA: what is the difference between business analytics and business intelligence and should we care?

In simple terms, business analytics is the new term that describes the activity or capability within the organisation to turn data into actions. Business intelligence has been part of those activities and capabilities and, while still a relevant term today, many use this to describe just the reports that feed the decisions that will lead to an action.

The business of analytics
Analytics is a discipline that applies logic and mathematics to data to provide insights for making better decisions. Many still struggle to understand the different analytical capabilities that can be leveraged.

In ‘Extend your portfolio of analytics capabilities’, Gartner defines four styles of analytics: descriptive, diagnostic, predictive, and prescriptive.

Descriptive analytics shows what is currently happening or has happened. It applies decisionmaking, so needs a human to be involved. All those reports that have been generated over the years – what some would call business intelligence – are actually descriptive analytics in practice.

Diagnostic analytics helps us understand why outcomes, events or trends occurred. This involves techniques such as visualisation, which leads to more questions and more insights, bringing you closer to a good decision. In business intelligence terms, this is known as Service BI, delivering discovery capabilities to different users within an organisation.

Predictive analytics anticipates future behaviour or estimates unknown outcomes. It tells us what will happen, anticipating future outcomes based on patterns that have been modelled, generated or detected. Some predictive analytics can even learn, giving us hints towards the right decisions that need to be made.

Prescriptive analytics specifies a preferred course of action. This applies both to situations when a human is in the loop, and for decision automation, when a system automatically carries out an action.

What many knew as business intelligence is really just the first two layers described above. It’s important to develop a portfolio of capabilities, often used together to address a wide range of problems.

The big data mess
By adding the term ‘big data’ into the mix, the waters get increasingly muddy. There is still a lot of ambiguity around what big data actually is and what value it brings to an organisation.

In a recent blog post I wrote after visiting clients in Asia, I responded to a question about how big the big data market is by pointing out there is actually no such thing as a big data market. It’s not a product or a service or a solution. It’s a concept that refers to a large volume of unstructured data which cannot be handled by standard database management systems.

However, there are technologies already accounted for in other areas that have the potential to make up a solution that deals with big data. For example, hardware components cater for high-volume storage and high movement of data. Organisations could be using these components today and not calling it big data. Big data technologies supplement – but do not replace – existing information management and analytics systems.

The starting point for any organisation is to work out what outcome it is trying to achieve. By knowing the desired outcome, you can reverse engineer the process to come up with the relevant questions that need to be asked. This then generates the relevant information needed to help give insight into the question.

I deliberately used the word ‘relevant’ because the challenge of handling big data is only going to continue to grow for many organisations, but will start to become easier once users understand what information is relevant to understanding the situations their business is facing.

Big data investments continued to rise, with 64 percent of organisations investing or planning to invest in technology for big data. However, fewer than eight percent have actually deployed that technology. Investments were led by organisations in the media and communications sectors, followed by banking and services.

When it comes to big data projects, unlike most business intelligence projects, the benefits are not just in decision making. Rather, these projects are geared to generate deeper business insights and optimise, automate or even design new processes. The range of uses spans marketing and sales growth; operational and financial performance improvement; risk and compliance management; new product and service innovation; and even direct/indirect data monetisation. Most often, value comes from combining diverse data to uncover patterns, links or phenomena that hasn’t previously been seen before.

The real question is: what value are organisations obtaining from looking at their data sources differently?

ABOUT IAN BERTRAM//
Ian Bertram is managing VP of Gartner’s analytics and business intelligence research team and head of research for Asia Pacific. Based in Sydney, he leads a global team of analysts who publish research and advise clients on analytics, business intelligence, information management, big data and performance management. Prior to joining Gartner, he was with IBM for 10 years.

Business intelligence: what are you really investing in?

Jennene Kelly — Mon, 16 Sep 2013 22:06:50 +0000

It’s widely recognised by business leaders that a change needs to happen: we need to treat information as an asset. But not all organisations treat information the same way or in fact use it the same way. Investment in business intelligence (BI) in the last decade has been seen as the way to make better use of this supposed ‘asset’.

Last year, Australian organisations spent more than A$436 million on BI software (including corporate performance management and analytics applications), an increase of 11.2% over 2011, and in New Zealand, spending grew 7.8 percent to NZ$68.7 million.

If you’re investing in BI today, what are you actually investing in? Are you investing in technology, are you investing in analytics, are you investing in organisational change, are you investing to help make a better decision, or are you investing in managing data more effectively?

Essentially it’s yes to all of these and more, but the extent of (and benefits from) your investment depends on the maturity of your organisation today and its willingness to bite the bullet on managing the information of tomorrow.

Organisations make hundreds if not thousands of decisions every day at different levels, with different degrees of impact, complexity, frequency and use. The only thing that’s constant is change, and BI and analytics represent a way of adapting to change. Identifying patterns, anticipating outcomes and responding proactively will be the basis of competition in the future.

Increasing analytics maturity
There are many styles of analytics needed for organisational decision making. It’s the combination of these styles that will increase the maturity of business analytics capabilities within an organisation.

Figure 1, outlines what we at Gartner call the analytics continuum, which explains the analytic styles from descriptive through to prescriptive analytics. Descriptive analytics is all those hundreds, if not thousands, of reports that we all generate but then never do anything with. I say that flippantly, but in many cases there’s a regulatory or compliance reason for having invested in the reports, so it’s being done for all the right reasons. It’s not money wasted, rather it’s a great foundation to build upon.

(Figure 1)

So far, 80 percent of analytics investments have been to produce historical/descriptive reports from information often aggregated in data warehouses and data marts. This is what has commonly been known as BI and investment will continue in this area. However, to get more value out of the data we manage, we then need to invest in diagnostic analytics such as interactive visualisation. This makes the data more consumable and usable.

Finally there are the more advanced styles of analytics such as predictive and prescriptive analytics. Predictive analytics uses technologies such as modelling, machine learning statistics and content analytics, while prescriptive analytics uses technologies such as decision/mathematical modelling, simulation and optimisation. These new styles have been pursued in silos within organisations today. That’s not necessarily a bad thing. The challenge is to harness the capabilities that are being developed and use those skills more broadly across the business.

There is a desire to shift capabilities towards the more advanced end of this analytic continuum, but skills will remain the biggest challenge for organisations. These additional capabilities often require new roles with additional quantitative, content and analytical skills, perhaps statistical analysts or data scientists.

Global trends
The global BI software market is highly fragmented with more than 50 vendors identified by Gartner with more than US$10 million in BI-software-related revenue. Despite this, it is fair to say that the market is dominated by five major players, led by SAP with 22.1% market share in 2012, followed by Oracle, IBM, SAS and Microsoft. Combined these market leaders dominate more than 70 percent of the BI software market globally.

There is a growing trend towards CIOs combining analytics with other technologies to create new capabilities, such as analytics plus supply chain for process management and improvement, analytics plus mobility for field sales and operations, and analytics plus social media for customer engagement and acquisition. New buying centres are also opening and expanding outside the IT department in line-of-business initiatives, and they are taking an increasingly large stake of the spending pie.

Two notable growth areas in the past 12-18 months are cloud-based buying and big data.

Cloud-based buying is showing substantial growth from a small base, especially for domainspecific analytic applications. Descriptive analytics has largely been completed for most big companies in traditional subject areas, such as finance and sales. As companies are now moving to more diagnostic use cases, growth opens up in numerous subject areas – such as HR, marketing, social and so on. New data sources, especially outside the firewall, have opened up for cloudbased vendors, which showed the most aggressive growth in 2012. However, cloud computing still accounts for a smaller portion of the BI market compared with other application markets.

Analytics plays squarely into the big data trend, where volume, velocity and variety aspects need to be taken into consideration. In that journey analytics pervade deeper in organisations, moving far outside the traditional ‘bread and butter’ BI domains and buying centres. But that also makes analytics bigger and more complex than we’ve been used to with traditional BI. Increased confusion around new hype terms and where the tangible benefits lie most likely contributed to a slowing of sales cycles in the BI market, while budget holders are trying to identify the tools and business value. In the medium term, we believe the much-hyped ‘big data’ will give way to what it has been all along – just ‘data’.

There is no doubt that organisations will continue to spend and earmark money for BI, despite constrained budgetary environments. What is becoming increasingly clear is that BI projects remain relatively shielded from budget cuts, while a healthy portion of any discretionary money will be available for upcoming analytic initiatives.

In short, organisations that are effective in adopting analytical capabilities and the integration between analytical capabilities and business processes are seeing lower costs and greater business impact from their investments.

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Ian Bertram – iStart keeping business informed on technology

Gartner untangles Analytics: BI, BA & big data

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Business intelligence: what are you really investing in?