infa16

#1 - Fragmented data #2 - Absence of holistic data management #3 - Product-centric approach as against Customer-centric

Most companies are not "data ready" simply because they're not ready to embrace data as a true asset. Shifting data culture from reactive to proactive takes significant leadership

People don't trust the data they have access to. They manage it centrally or fix it once. Things become out of balance and too much time is spent reconciling data #infa16

Data was often hoarded by siloed parts of the business "this is mine, that's yours". Cross-functional leadership must break those down starting with the CEO

Organizational issues not technology

You can see that the rise of the Chief Data Officer is a response to organizations trying to become data-ready, but it will take an entire company to working together to achieve this.

@debwalery exactly!

Data is not the end goal. Most companies struggle with translating data into business value e.g. data for customer purchase intent, data for customer sat, data for product benefit

@rbkarel I was just going to mention data hoarding! Big problem from poor quality of #data - believing others will misinterpret the data and make the wrong decision.

@monica1mullen DataHorders a new idea for Cable TV show..who's with me on this

@debwalery "Takes entire Company", So true!!

@furrier I'm in!

@furrier We'd watch that. Netflix binge even.

Lack of understanding of business processes by definition means you will be disconnected to the data an organization is generating. Time and again arguing about the data is senseless - the gap is in understanding the process that generated it.

There is a great stat that "90% of all the data in the world has been generated over the last two years" that @franzaman and others have been talking about. Just the volume of data today and what is to come is overwhelming.

imo unless data is priced on the balance sheet, investment to maintain it won't be easily connected across all management activities

That's the $64,000 question. Most organizations struggle to place value on data. I argue that the value is based on impact to business metrics (revenue, costs, risk, engagement, etc), others try to place a monetary value on data itself as well

i think you said it well in another thread that it is all about the data - data = value. if the data you are getting from #bigdata #datalake is of questionnable #quality, the value disappears.

here's a nice blog from a few years ago by a former colleague on putting a price on data http://blogs.teradat...

i am glad this question has come up - please don't let's confuse self-service autonomy with the simple act of using a silo'd #datapreparation tool - you need a foundation of #datamanagement #metadata

So self service goes far beyond Data Prep? Yes?

There is self-service and there is governed self-service. The former is the wild west, the latter - with a foundation built on a trusted #BigData Management platform addressing connectivity, quality and security concerns - can scale

yes exactly

I don't understand what self-service autonomy is. Can you define it pls

Make sure that it's #TrustedData in the #DataLake.

Fragmented & poor quality customer #data doesn’t deliver value. It is number 1 hindrance to achieving great #CX

The questions businesses have to answer these days come fast and furious and old style BI can take too long - by the time get the answer the question has changed. #infa16

@furrier self-service autonomy is the aspiration by business users to have little or no dependence on IT - however, the only way #datapreparation tools can deliver value is if there is a balance - business OWNS the data, IT makes sure there is #governance

@laura_wang you are right to mention old style #businessintelligence - in this day an age of #selfservice it is even more important that all the pretty dashboards and reports are reflecting good #quality data

@furrier here is a nice blog by my colleague on #datapreparation http://blogs.informa...

@parikhash thanks just wanted to be accurate on the definition

@furrier also - a lot of good content here on how @informatica Big Data Management and Intelligent Data Lake are pioneering a metadata driven and #datamanagement first approach https://www.informat...

This is the #BigData comparable to the #DataQuality sewage metaphor that we all know and love. It's simply a cute way of reminding everyone that garbage in/garbage out will always hold true

#DataGovernance #DataQuality and #MDM are still required for #BigData if you want the business to ever trust what you're doing in that #datalake

#dataswamps are discussed bc the data gets dirty if not relevant or understood in context

I hate the term data lake. Like #dataocean

Not sure it's a joke, but more that the lakes had become dumping grounds without the 3 pillars that @JohnM_Haddad mentioned earlier. Sort of a wild west mentality that originated with the promise of a #DataLake

@furrier - what people are really trying to say is that they have burned their hands trying to get #datalakes going - easy to stand it up but impossible to gain business value without #datamanagement #datagovernance #datasecurity

@monica1mullen totally agree with Monica on this; however that is the benefit of unstructured data to store it and figure it out; the key is don't let it turn to a swamp

@furrier So "lake" is too small a body of water to represent the morass of data?

@furrier #DataOcean works fine, as does anything else that will convince your leaders that value lives inside of it. Otherwise, it's just marketing (and I love marketing!)

Lake can also represent a loose configuration - a fluidity of the data. #bigdata isn't always about the volume.

@furrier "data lake" is the most commonly used term but there are many names orgs use such as analytics cloud, enterprise landing zone, enterprise data hub, data reservoir, audience hub, etc.

many data lakes are ungoverned which means it is difficult to find the data in the "swamp" & once you find it how do you know if you can trust the data

@Informatica Big Data Management includes universal metadata services to power semantic & faceted search, end-to-end data lineage, & collaborative data governance so your data lake does not become a data swamp

thanks for asking - this comes up all the time - it looks like this - http://www.computerw... #bigdata #datalake

people get started with manual approaches but soon realize that #datamanagement for small or #bigdata is no joke

@parikhash Surprised by stat: 70 percent of Hadoop deployments will fail to meet cost-savings and revenue-generation objectives

this is a "big" question :-)

Journey is the right word. No two #BigData paths need to be the same, starts with a business challenge that isn't fundamentally being addressed the "old way" and a pilot to challenge the status quo

there are many vectors we see kind of depends on the situation and environment and of course older and newer workloads

@rbkarel I'm reminded of the "Family Circus" cartoon, in which Billy takes a journey from school to home

The key to a successful #BigData journey is to go from sandbox or pilot to measurable benefits #ROI in a logical way

exactly and well said @RBKarel - what starts out as an experiment tends to remain an experiment due to the lack of due diligence around #datamanagement for #bigdata

often starts with data warehouse optimization, then an analytics data lake, then onward towards real-time and IoT.

one journey is that unstructured data drives the conversation - say #cloudnative or app specific workload

increasingly, the journey is starting when companies are looking to move some of their traditional analytical workloads to #bigdata environments for better ROI @infaworld

Use @ to reply A key mistake would be to "pave the cowpath" ie, thinking #bigdata is just an extension of what is currently being done

On hadoop it's clear from our data: two inhibitors are in play - 1) it's too hard to use and 2) total cost of ownership is high

if something is hard to do and cost more then it's not setup to scale but fail

many customers are seeking to build a marketing data lake to improve customer engagement and campaign effectiveness. They start with a few data sources and then add more to increase value

@furrier @RBKarel I heard once from @TCEDataDiva - Right level of #DataGovernance in #DataLake to avoid turning into swamp

@rbkarel I agree it's perfect; beauty is in the eye of the beholder in terms of data.

@furrier i think it's important to establish one thing - #bigdata #datalake have a ton of promise - a lot of business value can be gained IF done the right way as we have been discussing