Extracting Value from Big Data

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In 2010, most executives were not in the least interested in taking on board advice that data was a strategic asset that needed investment if they were to survive in the future.

Two years on, and the executive team are finally sitting up and taking notice that data will define their future. Is it too late for these lagging companies to maintain or regain their strategic ground.

Studies have shown that those companies that have embraced a data-driven decision culture, and have incorporate big data into their information strategy significantly outperform those who have failed to heed the signals that data will indeed drive strategic advancement.

Personally, I don’t think it is too late – but only because Cloud technologies and Data/Analytics-as-a Service have conveniently bridged the timeline gap. However, in promoting this, there can be no further delay in the acquisition of insight that data analytics delivers.

Cloud computing and data as a service allow companies to federate proprietary data with purchased Big Data [data from social data social networks, bloggers, statistics, sensors, logs etc]. Gaining a  360-degree view of customer demographics, economics and preferences provides broader and deeper actionable insight that can be transformed into marketing campaigns and product devleopment.

  • Marketing campaigns can now be highly targeted, due to insight that predicts customer behaviour and preference trends.
  • Product features can be optimised based on customer preferences.

These insight translate right throughout the supply chain from demand planning and optimisation right down to in-store merchandising.

Valid insight, imbued with relevant context is the key to extracting value from Big Data. Together, with supporting culture and processes that ensure that this value is driven into strategic and operational improvement will seal the ongoing success of any business.

Update on Top 6 BI Trends for 2012

Analyst Reports, Big Data, Big Data Analytics, Cloud BI No Comments

Late in 2011, Gartner came out with their top 6 predictions for business intelligence: BI in the Cloud, Mobile BI, Analytics, Agile BI, In-memory analytics, and Big Data. As we enter the final quarter, let’s take a look at how things are panning out.

BI in the Cloud

As Cloud computing continues to dominate the IT landscape, Gartner is predicting that cloud offerings will make up just 3 per cent of BI revenue by 2013. However, it is gaining ground and chipping away at on-premises BI, particularly in the Big Data Analytics space.
Decision makers are still questioning the Cloud as an operational tool. The initial move of data to the Cloud is a challenge, as is bandwidth and security. Many are favoring using Cloud of back up storage and archiving, and disaster recovery, but are still unsure of how well Cloud will support operational data activities.

Mobile BI

Forrester’s believe Mobile BI will go mainstream in 2012. Whilst I agree that mobile BI will continue to grow, the small device platform is suited only to a limited subset of business intelligence activities. There is still an issue around data security on caching data on mobile devices, which are readily lost or stolen. There are also processing power constraints, limiting any real value with analytics.
As a quick, on the go monitoring tool – great – but I am unconvinced that mobile devices will suit the escalating swell in analytics needs.


The MIT Sloan Management report found that organizations using analytics are more than twice as likely to substantially outperform their competitive peers. Analytics is certainly the one to watch. As many companies are still struggling to publish dashboards on historical data – the technical needs around advanced analytics are still well beyond many organisations. This is certainly one for the Cloud – and watch out for a rapid explosion of Analytics as a Service offerings. Moving analytics closer to the data is a key strategy, and with Big Data better suited to the Cloud – it makes sense that analytics will follow.

Analytics has already taken off, it is now a matter of maturity towards optimization and prediction becoming integrated into business processes so that it is consumable as a decision management tool.

Agile BI

Agile is good with anything today. Methodologies are not getting a lot of focus as BI teams are getting swamped with requests for reports and dashboards that are better off being done locally, but business users. However, sadly too little attention has been given to training in using BI tools and in effective dashboard design. Before agile BI can really take hold, BI teams need to get more aligned with their real function – and pass publishing back to the business.

In-Memory Analytics

Memory continues to get cheaper by the day – and in-memory analytics tools such as Qlikview, Spofire and Tableau are helping users gain more insight into the power of simple data exploration. The real benefit, is that local data sets can be used, rather than relying on waiting for IT to import data into centralized data warehouses. This does cut out the data quality look – but for exploration purposes it’s a good start to helping businesses to expand their business intelligence portfolio.

Big Data

Forrester’s prediction that Big Data will move out of the silos and into enterprise IT may be right, but its not the best decision for many companies. With the rapid growth in the volume and variety of data it makes much more sense for Big Data to be in the Cloud. Corporates have enough to do without continually adding more servers and memory.

Big Data is certainly changing the business landscape – however, its maturity has yet to cross ‘The Chasm’ and corporate IT is better to wait until it has, and for their business users to get more savvy about how to use Big Data.

Big Data Analytics – Moving From Prediction to Possibility

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To date, BI analytics tools have largely been used on historical data to predict likely outcomes of various use case scenarios. In such instances, the use of historical data adds a dimension of probability to the statistical realm.

Big data is not historical data – it is Now Data – and rather than be bound by prediction, or even by probability, Big Data Analytics takes a look at the possible.

One of my gripes with so-called ‘men of science’ is that they refuse to accept that something is possible unless there is a way to define it in terms of empirical evidence that can be replicated with consistent outcomes. Whilst I understand the value of ’empirical science’ for many instances, I don’t feel comfortable with such a closed-minded approach to everything.

My belief is – ‘just because no-one has yet determined how to measure something, or indeed replicate its existence or impact, doesn’t mean to say that it doesn’t exist’, or is not possible.

Finally with Big Data Analytics, I feel my view is gaining substance. There are a number of scientists today researching Quantum Physics who also stand beside me – they know that certain things occur in fields of energy, they just cannot replicate it in a way that enables them to define it with certainty or at least control it with reasonable probability.

Are we finally moving along the continuum from Prediction > Probability > Possibility?

Is Your Data Too Big For You?

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Most companies are drowning in their own transactional data, let alone worrying about Big Data. Big Data is too diverse to be analyzed using conventional methods. Extracting value from Big Data takes a new mind set and a new toolset. So is it worth it?

According to those who have taken the step – the answer is yes. Over 45% of big data is being used for marketing; to create personalized customer experiences, to gain insight into how social influence is affecting customer behavior, and to optimize multi-channel marketing spend using highly targeted messaging.

The ability to detect early warning signals is proving to be a revolutionary guide to developing new products and services. Businesses have always known that these signals, or triggers were out there – but had no way to capture them, and connect them in a meaningful way. Big Data tools have changed that.

Digital marketing is still growing rapidly – forecast to grow from $34B to $76 B by 2016. It is providing evidence of impact footprints not previously measurable.

The cautionary note is that implementing the tools is a lot easier than changing the mindset of users. Adopting a culture of data-driven decision making takes time – and for many managers, a leap of faith. In a world already shattered by uncertainty, this is not an easy leap. But the dividends seem to be paying off.

Do you have experiences with using Big Data effectively you can share?

Just What is a Data Scientist?

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In the emerging world of Big Data, one term making an entrance is ‘Data Scientist’. But, just what is a Data Scientist? I did a bit of digging around to find out. This is what I found:

First, let’s start with what it is not – a Business Analyst or a Data Analyst.

Although this ‘new role’ has emerged with the hype around Big Data,  it is not exclusive to Big Data projects. According to IBM, “A data scientist represents an evolution from the business or data analyst role”. Not sure I agree with this. Both of these roles are highly varied in skills and responsibilities. I would tend to suggest that Read the rest…

Big Data Getting Attention in C-Suite

Analytics, Big Data, Data No Comments

According to a recent survey of executives at Fortune 1000 companies and large government agencies, the C-suite has high hopes for the value that analytics on Big Data promises, but is this just a wild pipe dream?

The survey revealed that eighty-five percent of respondents expected to gain substantial business and IT benefits from Big Data initiatives, with the main expected benefits being ‘fact-based decision making’ and ‘Customer experience’. Sound familiar so far?

Whilst it is encouraging to read companies are raising their hopes of BI to have a “positive impact across multiple lines of business” the recorded constraints in Big Data Analytics are the same for any BI implementation over the last 10 years, namely: Read the rest…