Three of four architects strive to make their firms data driven. But well-meaning technology managers only deal with part of the problem: How to use technology to glean deeper, faster insight from more data -- and more cheaply. But consider that only 29% of architects say their firms are good at connecting analytics results to business outcome. This is a huge gap! And the problem is the ‘data driven’ mentality that never fights it’s way out of technology and to what firms care about - outcomes.
In 2016, customer-obsessed leaders will leapfrog their competition, and we will see a shift as firms seek to grow revenue and transform customer experiences. Insight will become a key competitive weapon, as firms move beyond big data and solve problems with data driven thinking.
Shift #1 - Data and analytics energy will continue drive incremental improvement
In 2016, the energy around data-driven investments will continue to elevate the importance of data and create incremental improvement in business performance. In 2016, Forrester predicts:
Chief data officers will gain power, prestige and presence...for now. But the long term viability of the role is unclear. Certain types of businesses, like digital natives, won’t benefit from appointing a CDO.
Machine learning will reduce the insight killer - time. Machine learning will replace manual data wrangling and data governance dirty work. The freeing up of time will accelerate data strategies.
You can't bring up semantics without someone inserting an apology for the geekiness of the discussion. If you're a data person like me, geek away! But for everyone else, it's a topic best left alone. Well, like every geek, the semantic geeks now have their day — and may just rule the data world.
It begins with a seemingly innocent set of questions:
"Is there a better way to master my data?"
"Is there a better way to understand the data I have?"
"Is there a better way to bring data and content together?"
"Is there a better way to personalize data and insight to be relevant?"
Semantics discussions today are born out of the data chaos that our traditional data management and governance capabilities are struggling under. They're born out of the fact that even with the best big data technology and analytics being adopted, business stakeholder satisfaction with analytics has decreased by 21% from 2014 to 2015, according to Forrester's Global Business Technographics® Data And Analytics Survey, 2015. Innovative data architects and vendors realize that semantics is the key to bringing context and meaning to our information so we can extract those much-needed business insights, at scale, and more importantly, personalized.
Industry-renowned data visualization expert Edward Tufte once said: "The world is complex, dynamic, multidimensional; the paper is static, flat. How are we to represent the rich visual world of experience and measurement on mere flatland?" He's right: There's too much information out there for knowledge workers to effectively analyze — be they hands-on analysts, data scientists, or senior execs. More often than not, traditional tabular reports fail to paint the whole picture or, even worse, lead you to the wrong conclusion. AD&D pros should be aware that data visualization can help for a variety of reasons:
Visual information is more powerful than any other type of sensory input. Dr. John Medina asserts that vision trumps all other senses when it comes to processing information; we are incredible at remembering pictures. Pictures are also more efficient than text alone because our brain considers each word to be a very small picture and thus takes more time to process text. When we hear a piece of information, we remember 10% of it three days later; if we add a picture, we remember 65% of it. There are multiple explanations for these phenomena, including the fact that 80% to 90% of information received by the brain comes through the eyes, and about half of your brain function is dedicated directly or indirectly to processing vision.
We can't see patterns in numbers alone . . . Simply seeing numbers on a grid doesn't always give us the whole story — and it can even lead us to draw the wrong conclusion. Anscombe's quartet demonstrates this effectively; four groups of seemingly similar x/y coordinates reveal very different patterns when represented in a graph.
Get ready for AWS business intelligence (BI): it's real and it packs a punch!
Today’s BI market is like a perpetual motion machine — an unstoppable engine that never seems to run out of steam. Forrester currently tracks more than 50 BI vendors, and not a month goes by without a software vendor or startup with tangential BI capabilities trying to take advantage of the craze for BI, analytics, and big data. This month is no exception: On October 7, Amazon crashed the party by announcing QuickSight, a new BI and analytics data management platform. BI pros will need to pay close attention, because this new platform is inexpensive, highly scalable, and has the potential to disrupt the BI vendor landscape. QuickSight is based on AWS’s cloud infrastructure, so it shares AWS characteristics like elasticity, abstracted complexity, and a pay-per-use consumption model. Specifically, the new QuickSight platform provides
New ways to get terabytes of data into AWS
Automatic enrichment of AWS metadata for more effective BI
An in-memory accelerator (SPICE) to speed up big data analytics
An industrial grade data analysis and visualization platform (QuickSight), including mobile clients
Consumers (and B2B customers) are more and more empowered with mobile devices and cloud-based, all but unlimited access to information about products, services, and prices. Customer stickiness is increasingly difficult to achieve as they demand instant gratification for their ever changing tastes and requirements. Switching product and service providers is now just a matter of clicking a few keys on a mobile phone. Forrester calls this the age of the customer, which elevates business and technology priorities to achieve:
Business agility.Business agility often equals the ability to adopt, react, and succeed in the midst of an unending fountain of customer driven requirements. Agile organizations make decisions differently by embracing a new, more grass-roots-based management approach. Employees down in the trenches, in individual business units, are the ones who are in close touch with customer problems, market shifts, and process inefficiencies. These workers are often in the best position to understand challenges and opportunities and to make decisions to improve the business. It is only when responses to change come from these highly aware and empowered employees, that enterprises become agile, competitive, and successful.
Ah, the good old days. The world used to be simple. ETL vendors provided data integration functionality, DBMS vendors data warehouse platforms and BI vendors concentrated on reporting, analysis and data visualization. And they all lived happily ever after without stepping on each others’ toes and benefiting from lucrative partnerships. Alas, the modern world of BI and data integration is infinitely more complex with multiple, often overlapping offerings from data integration and BI vendors. I see the following three major segments in the market of preparing data for BI:
Fully functional and highly scalable ETL platforms that are used for integrating analytical data as well as moving, synchronizing and replicating operational, transactional data. This is still the realm of tech professionals who use ETL products from Informatica, AbInitio, IBM, Oracle, Microsoft and others.
An emerging market of data preparation technologies that specialize mostly in integrating data for BI use cases and mostly run by business users. Notable vendors in the space include Alteryx, Paxata, Trifecta, Datawatch, Birst, and a few others.
Data preparation features built right into BI platforms. Most leading BI vendors today provide such capabilities to a varying degree.
My colleague Henry Baltazar and I have been watching the development of new systems and storage technology for years now, and each of us has been trumpeting in our own way the future potential of new non-volatile memory technology (NVM) to not only provide a major leap for current flash-based storage technology but to trigger a major transformation in how servers and storage are architected and deployed and eventually in how software looks at persistent versus nonpersistent storage.
All well and good, but up until very recently we were limited to vague prognostications about which flavor of NVM would finally belly up to the bar for mass production, and how the resultant systems could be architected. In the last 30 days, two major technology developments, Intel’s further disclosure of its future joint-venture NVM technology, now known as 3D XPoint™ Technology, and Diablo Technologies introduction of Memory1, have allowed us to sharpen the focus on the potential outcomes and routes to market for this next wave of infrastructure transformation.
Gene Leganza and I just published a report on the role of the Chief Data Officer that we’re hearing so much about these days – Top Performers Appoint Chief Data Officers. To introduce the report, we sat down with our press team at Forrester to talk about the findings, and the implications for our clients.
Forrester PR: There's a ton of fantastic data in the report around the CDO. If you had to call out the most surprising finding, what would top your list?
Gene: No question it's the high correlation between high-performing companies and those with CDOs. Jennifer and I both feel that strong data capabilities are critical for organizations today and that the data agenda is quite complex and in need of strong leadership. That all means that it's quite logical to expect a correlation between strong data leadership and company performance - but given the relative newness of the CDO role it was surprising to see firm performance so closely linked to the role.
Of course, you can't infer cause and effect from correlation – the data could mean that execs in high-performing companies think having a CDO role is a good idea as much as it could mean CDOs are materially contributing to high performance. Either way that single statistic should make one take a serious look at the role in organizations without clear data leadership.
In the past three decades, management information systems, data integration, data warehouses (DWs), BI, and other relevant technologies and processes only scratched the surface of turning data into useful information and actionable insights:
Organizations leverage less than half of their structured data for insights. The latest Forrester data and analytics survey finds that organizations use on average only 40% of their structured data for strategic decision-making.
Unstructured data remains largely untapped. Organizations are even less mature in their use of unstructured data. They tap only about a third of their unstructured data sources (28% of semistructured and 31% of unstructured) for strategic decision-making. And these percentages don’t include more recent components of a 360-degree view of the customer, such as voice of the customer (VoC), social media, and the Internet of Things.
BI architectures continue to become more complex. The intricacies of earlier-generation and many current business intelligence (BI) architectural stacks, which usually require the integration of dozens of components from different vendors, are just one reason it takes so long and costs so much to deliver a single version of the truth with a seamlessly integrated, centralized enterprise BI environment.
Existing BI architectures are not flexible enough. Most organizations take too long to get to the ultimate goal of a centralized BI environment, and by the time they think they are done, there are new data sources, new regulations, and new customer needs, which all require more changes to the BI environment.
The explosion of data and fast-changing customer needs have led many companies to a realization: They must constantly improve their capabilities, competencies, and culture in order to turn data into business value. But how do Business Intelligence (BI) professionals know whether they must modernize their platforms or whether their main challenges are mostly about culture, people, and processes?
"Our BI environment is only used for reporting — we need big data for analytics."
"Our data warehouse takes very long to build and update — we were told we can replace it with Hadoop."
These are just some of the conversations that Forrester clients initiate, believing they require a big data solution. But after a few probing questions, companies realize that they may need to upgrade their outdated BI platform, switch to a different database architecture, add extra nodes to their data warehouse (DW) servers, improve their data quality and data governance processes, or other commonsense solutions to their challenges, where new big data technologies may be one of the options, but not the only one, and sometimes not the best. Rather than incorrectly assuming that big data is the panacea for all issues associated with poorly architected and deployed BI environments, BI pros should follow the guidelines in the Forrester recent report to decide whether their BI environment needs a healthy dose of upgrades and process improvements or whether it requires different big data technologies. Here are some of the findings and recommendations from the full research report: