Sub-second analytical BI time to value still a pipe dream

Internet search engines with instant query responses may have misled enterprises into believing all analytical queries should deliver split second answers.

With the advent of Big Data analytics hype and the rapid convenience of internet searches, enterprises might be forgiven for expecting to have all answers to all questions at their fingertips in near real time.

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Unfortunately, getting trusted answers to complex questions is a lot more complicated and time consuming than simply typing a search query. Behind the scenes on any internet search, a great deal of preparation has already been done in order to serve up the appropriate answers. Google, for instance, dedicates vast amounts of high-end resources and all of its time to preparing the data necessary to answer a search query instantly. But even Google cannot answer broad questions or make forward-looking predictions. In cases where the data is known and trusted, the data has been prepared and rules have been applied, and the search parameters are limited, such as with a property website, almost instant answers are possible, but this is not true BI or analytics.

Within the enterprise, matters become a lot more complicated.  When the end-user seeks an answer to a broad query – such as when a marketing firm wants to assess social media to find an affinity for a certain range of products over a 6-month period – a great deal of ‘churn’ must take place in the background to deliver answers. This is not a split-second process, and it may deliver only general trends insights rather than trusted, quality data that can serve as the basis for strategic decisions.

When the end user wishes to do a query and is given the power to process their own BI/Analytics, lengthy churn must take place. Every time a query, report or instance of data access is converted into useful BI/Analytical information for end-consumers, there is a whole lot of preparation work to be done along the way : i.e. identify data sources>  access> verify> filter> pre-process>  standardize> lookup> match> merge> de-dup> integrate> apply rules> transform> preprocess> format> present> distribute/channel.

Because most queries have to traverse, link and process millions of rows of data and possibly trillions of words from within the data sources, this background churn could take hours, days or even longer.

A recent TWDI study found that organisations are dissatisfied with the time it takes for the chain of processes involved for BI, analytics and data warehousing to deliver valuable data and insights to business users. The organisations attributed this, in part, to ill-defined project objectives and scope, a lack of skilled personnel, data quality problems, slow development or inability to access all relevant data.

The problem is that most business users are not BI experts and do not all have analytical minds, so the discover and report method may be iterative (therefore slow) and in many cases the outputs/results are not of the quality expected. The results may also be inaccurate as data quality rules may not have been applied, and data linking may not be correct, as it would be in a typical data warehouse where data has been qualified and pre-defined/derived. In a traditional situation, with a structured data warehouse where all the preparation is done in one place, and once only, and then shared many times, supported by quality data and predefined rules, it may be possible to get sub-second answers. But often even in this scenario, sub-second insights are not achieved, since time to insight also depends on properly designed data warehouses, server power and network bandwidth.

Users tend to confuse search and discover on flat raw data that’s already there, with information and insight generation at the next level. In more complex BI/Analytics, each time a query is run, all the preparation work has to be done from the beginning and the necessary churn can take a significant amount of time.

Therefore, demanding faster BI ‘time to value’ and expecting answers in sub-seconds could prove to be a costly mistake. While it is possible to gain some form of output in sub-seconds, these outputs will likely not be qualified, trusted insights that can deliver real strategic value to the enterprise.

By Mervyn Mooi, Director at Knowledge Integration Dynamics (KID)

 

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Big data best practices, and where to get started

Big data analytics is on the ‘to do’ list of every large enterprise, and a lot of smaller businesses too. But perceived high costs, complexity and the lack of a big data game plan have hampered adoption in many South African businesses.

By Mervyn Mooi, Director, The Knowledge Integration Dynamics Group

Big data as a buzzword gets thrown around a great deal these days. Experts talk about zettabytes of data and the potential goldmines of information residing in the wave of unstructured data circulating in social media, multimedia, electronic communications and more.

As a result, every business is aware of big data, but not all of them are using it yet. In South Africa, big data analytics adoption is lagging for a number of reasons: not least of them, the cost of big data solutions. In addition, enterprises are concerned about the complexity of implementing and managing big data solutions, and the potential disruptions these programmes could cause to daily operations.

It is important to note that all business decision makers have been using a form of big data analytics for years, whether they knew it or not. Traditional business decision making has always been based on a combination of structured, tabular reports and a certain amount of unstructured data – be that a phone call to consult a colleague or a number of documents or graphs – and the analytics took place at the discretion of the decision maker. What has changed is that the data has become digital; it has grown exponentially in volume and variety, and now analytics is performed within an automated system. To benefit from the new generation of advanced big data analytics, there are a number of key points enterprises should keep in mind:

  • Start with a standards-based approach. To benefit from the almost unlimited potential of big data analytics, enterprises must adopt an architected and standards-based approach for data / information management implementation which includes business requirements-driven integration, data and process modeling, quality and reporting, to name a few competencies.

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In context of an organized approach, an enterprise first needs to determine where to begin on its big data journey. The Knowledge Integration Dynamics Group is assisting a number of large enterprises to implement their big data programmes, and we have formulated a number of preferred practices and recommendations that deliver almost instant benefits and result in sustainable and effective big data programmes.

  • Proof of Concept unlocks big value. Key to success is to start with a proof of concept (or pilot project) in a department or business subject area that has the most business “punch” or is of the most importance to the organisation. In a medical aid company, for example, the claims department or business might be the biggest cost centre and with the most focus. The proof of concept or pilot for this first subject area should not be a throwaway effort, but rather a solution that can later be quickly productionised, with relevant adjustments, and reused as a template (or “foot-print”) for programmes across the enterprise.
  • Get the data, questions and outputs right. Enterprises should also ensure that they focus on only the most relevant data and know what outputs they want from it. They would have to carefully select the data/information for analytics that would give the organisation the most value for the effort. Furthermore, the metrics and reports that the organisation generates and measures itself by, must also be carefully selected and adapted to specific business purposes. And of course, the quality and trust-worthiness of sourced data/ information must be ensured before analytical models and reports are applied to it.
  • Get the right tools. In many cases, enterprises do not know how to apply the right tools and methodologies to achieve this. Vendors are moving to help them by bringing to market templated solutions that are becoming more flexible in what they offer, so allowing organisations to cherry pick the functionality, metrics and features they need. Alternatively, organisations can have custom solutions developed.
  • It’s a programme, not a project. While proof of concepts typically show immediate benefits, it is important for organisations to realise that the proof of concept is not the end of the journey – it is just the beginning. Implementing the solution across the enterprise requires strategic planning, adoption of a common architected approach (e.g. to eliminate data siloes and wasted / overlapping resources), and effective change management and collaboration initiatives to overcome internal politics and potential resistance and ensure the programme delivers enterprise-wide benefits.