Why Big Data Analytics needs BI Too

TammyPowlas · ‎04-13-2014

Barry Devlin of 9sight Consulting gave this BrightTalk webcast last week. With his permission (and BrightTalk), I share my notes below.

The abstract read:

“Business and IT are facing the challenge of getting real and urgent value from ever-expanding information sources. Building independent silos of big data analytics is no longer enough. True progress comes only by integrating data from traditional operational and informational sources with the new sources that are becoming available, whether from social media or interconnected machines.” You can watch the replay at https://www.brighttalk.com/webcast/9059/95949

Figure 1: Source: 9sight Consulting

Figure 1 shows how Big Data Analytics began – to understand and track what is going on

Now we have new information. This is simple, but new source of data

There are two pieces : basic BI and the other is operational BI real time insight into web site . An example is why a customer would abandon a cart in real time

What most likely buy if cross sell or up sell

Figure 2: Source: 9sight Consulting

Figure 2 shows how IoT drives huge quantities of data, with opportunities to re-invent business and create new businesses entirely

It extends the supply chain to the consumer – internet connected refrigerator – capable of monitoring goods inside, and can tell supplier what need to buy next

New business process is motor insurance, to spread the risk type of model with a sensor in car report on driving behavior

With health monitoring you bring people to hospital – but now you can monitor them at home to measure vital signs

This raises privacy and security issues, which is what big data does

Figure 3: Source: 9sight Consulting

You still need traditional business data

This includes the legal business of data that we did this business, we shipped this business, we invoiced you, time to pay

Big data is usually unreliable sources, unrelated

Figure 4: Source: 9sight Consulting

The picture on the right of Figure 4 is from a 1988 IBM Systems Journal with layered architecture

Tactical decision making was made based on reconciled data

This is now superceded by speed

Figure 5: Source: 9sight Consulting

Figure 5 shows new types of data: machine generated data, human sourced

On the horizontal line of the image on the right of Figure 6 shows the timeliness consistency

The vertical line shows structured content

Bottom left for machine generated data includes sensors, which is a major direction for IoT

On the top is human sourced information, including personal experience. This is subjective and reflects personal experience such as tweets to videos

Process mediated data has 2 arrows pointing to it. Before the internet we were capturing data from machines, and capturing from people. Machines that were inside our businesses – an ATM - machine, signals, and bring into process mediated form. The human source is clerk in bank, taking down information – customer, name, address – turned into process mediated data

When we put it through business processes is turns into process mediated data

Do we bring everything into process mediated data? This is not right answer

With modern machine data and human data, the characteristic is uncertainty

We need the ability to treat these three types of data differently

Figure 6: Source: 9sight Consulting

Figure 6 shows a modern IT environment, in a logical way

REAL means real extensible actionable labile (flexible)

Sources include measures, events, and messages, which instantiate create transactions and are less well managed and puts them into pillars

Figure 7: Source: 9sight Consulting

Figure 7 shows you can integrate sources in stores through this architecture

With operational processes, you create transactions part of the legal flow of the business

The box in middle of Figure 7 shows assimilation

These are pillars rather than layers

This is different than data lake or reservoir

Figure 8: Source: 9sight Consulting

Figure 8 shows the relational model as the core model

Columnar, compressed – ability to do different types of processing than row based

- Reduced physical modeling
- Faster read write
- Queries running hundreds of times faster

Figure 9: Source: 9sight Consulting

On this slide he mentioned there is a white paper on eBay.

eBay used a relational database to take machine generated data

Figure 10: Source: 9sight Consulting

Hadoop is key technology for handling human sourced data

Information is soft and lacking in known structures, large, ill defined

This is an evolving area, more diverse. It is largely batch; Hadoop 2.0 enables more real time

It is a programming environment – whereas is BI/EDW – declarative (descriptions) versus procedural approach of Hadoop

There is a push towards human sourced content

Figure 11: Source: 9sight Consulting

Three types of processing shown on Figure 11

It spans all of IT, not just IT

Instantiation turns measures events messages into instances

- Also include operational processes
- Transformed events coming into transaction
- Create an operational program in SAP takes events and makes them transactions
- File access – human source

In the middle is assimilation: reconcile information before making it useful for business

Reification makes abstract real like data virtualization

Figure 12: Source: 9sight Consulting

Metadata is not data, it is information and much softer than data

It describes processes, people

For non-IT – it indistinguishable from information

NSA stole the word and made it big news

CSI (not the TV show) – provides information to bring across pillars to ensure talking the same language across the pillars; communicating instead of simply processing

MARS metadata error with programs in Orbiter – metric, on ground, imperial measures (page 76 of his book)

You still do up front modeling – for those that we understand and know and the rest is done with text mining

Figure 13: Source: 9sight Consulting

Figure 13 shows his book, which I do own but have not read it yet. The book came highly recommended to me.

Figure 14: 9sight Consulting

The conclusion is in Figure 14.

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Why Big Data Analytics needs BI Too

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