Crystal Gaze 2021: Predictions From Ashley Kramer, Chief Product and Marketing Officer at Sisense
Customer analytics and customer experience are driving the technology industry. Ashley Kramer, Chief Marketing Officer and CPO at Sisense chatted with us about the key trends and BI predictions that influence the innovations in BI and Data Science industry.
Here’s the full interview with CMO Sisense, Ashley Kramer.
Hi, Ashley Kramer. Tell us how ANZ market is shaping up and how you see the COVID-19 crisis playing a greater role in Data Management industry.
COVID has made data even more vital to business success moving into 2021
The global pandemic has driven home the fact that data is vital to the success of every organization. Companies across Australia and New Zealand (ANZ) are realizing the importance of scaling and growing their analytics capabilities — something that has only become even more important in the COVID era.
Ashley says, “Sisense recently surveyed over 460 companies across Australia and New Zealand to dig into their data and analytics usage and future plans. The results reinforce how critical analytics are to businesses in the region, both to succeed in the current environment and to grow in the future.”
More than ever, companies across Australia and New Zealand are leaning on data analytics to help drive better decision-making and strengthen their businesses against the current volatile economic landscape.
In fact, 67% of respondents view BI and analytics programs as more or much more important to business operations now than before the pandemic. The results also demonstrate that marketing, operations, finance and HR are making the biggest inroads in their use of analytics and employing business intelligence to inform decision-making. 55% of companies are using data sources, analytics and dashboards more often or much more often than before COVID-19.
How are Business Intelligence tools enabling the Marketing department to make better decisions? How are other departments in an organization performing in comparison?
Marketing is leading this charge, with more than 50% of organizations reporting the marketing department is currently performing analytics or employing BI solutions, followed by operations (40%), finance (33%), and HR (31%). 40% of respondents also pointed to marketing as the department to be adding BI and analytics solutions as a result of COVID-19.
Sisense expects marketing to continue to lead this charge well into the coming years.
From a budgeting perspective, 78% will either maintain or increase spend on BI/data analytics initiatives, including software, tools, time, and team members, in the near future, and nearly all organizations (99.5%) are developing new use cases for data to maintain business continuity.
47% are using data to improve efficiency, 40% to identify new revenue streams, 29% to optimize supply chains and 29% to reduce expenses.
One this is very clear, for competitive advantage moving into 2021, data will make the difference.
Tell us how we are witnessing the rise of the ‘data team’.
Smart organizations already appreciate the power of data and its influence on building successful strategies. Their challenge isn’t to convince decision-makers that they need advanced analytics. Instead, they need to ensure their organizations benefit fully from all the data at their fingertips. Hence the rise of the data team!
Sisense reports 90% of all existing data has been generated in the last two years alone, and it’s anticipated that the global datasphere will expand from about 44 zettabytes (ZB) in 2020 to 175 ZB by 2025.
That’s an immense amount of data to capture and manage, and it’s coming from more sources and in more formats than ever before. The problem is that there’s so much of it, and it’s being created so quickly, that it’s difficult to keep track of it all. Only about 9 ZB of this mass of data may actually be stored, and only about one-third of that will actually be used.
Data-savvy organizations know that this is unacceptable, because neglected and missing data could contain the key to their competitive edge.
Tell us more about the Data Management processes and data lifecycle in the current context?
Data teams will rise in importance because they know best how to work with this complex data: Capturing, Managing, Organizing, and Integrating it, then finally turning it into powerful strategic and predictive insights that go beyond basic business reporting.
With the available data, each business team from any function within an organization can understand what is happening in more granular detail and more accurately predict what will happen and how to get there.
Data teams are the key to cutting through the complexity of your business’s challenges.
They’re adept at combining disparate sources of data when more complex questions come up. These increasingly difficult questions require sophisticated data models, connected to an increasing number of data sources, in order to produce meaningful answers.
Therein lie the power of the data team: Armed with know-how, they connect with the end-user teams (internal users, product teams embedding insights, etc.) and get to understand their needs so that they can build data models and connect to the data sources that will deliver the greatest benefit to the entire company.
Nowadays, businesses are built on the ability to ask sophisticated questions and derive clear insights from complex data. No wonder, then, that data — and the data teams’ role in capturing, organising, and interpreting it — is increasingly becoming an essential part of any organization. Now that data has become ascendant, it truly is time for the rise of the data teams.
What according to you are the top tech trends for 2021 – VR/AR/XR, AI, LM, IoT, Bots, and what else are you most keenly following?
Adaptable AI is the most exciting emerging technology.
If you have data, odds are you have a lot of it. You’ve probably got more than you can handle. Only AI will be able to help humans make sense of the huge datasets being generated every day by countless individuals and devices. AI systems will play greater and greater roles in our personal and business worlds, and as such, the AI has to adapt to meet demand.
Augmentation and reinforcement learning are much more powerful than out-of-the-box solutions, and this is the future of AI. Planning for every feature starts with questions about how the user will be able to play around with and modify the input to see how it affects the result. Sisense has put significant investment into knowledge graphs, Natural Language Processing ( NLP), and automated machine learning.
Together, they enable users to actively engage with the system, enjoying recommendations along with analysis. These features also facilitate a positive feedback loop, using engagement to strengthen what works and get rid of what doesn’t.
One result is systems become much more intuitive:
Users can take advantage of the “Simply Ask” feature to check “what are my sales next two months” and receive chatbot messages with projected visualizations and suggestions for further exploration routes. In a similar way, the forthcoming “Explanations” feature provides users with possible drivers of the movements in the data automatically, using knowledge graphs to go beyond the boundaries of their charts. This can turn the problem definition environment to multidimensional and learn from the user interaction with the system to personalise and match the results.
How do you see the evolution of data visualization techniques amplifying “storytelling ideas” in 2021?
For me, less dashboarding and more storytelling.
Dynamic data stories will replace static dashboards into the future, Sisense says. At Amazon, everyone in a meeting sits down at the beginning and reads a full write-up, and then the discussion begins, rather than sitting through an endless PowerPoint presentation during the whole meeting. They focus on real storytelling rather than bullet points.
Sisense expects something similar to happen with dashboards: fetching insights-driven digests just in time, but also accompanying the daily routines with an “agent” supporting business flows in various tools.
The world is moving from the static, rigid experience to the data, insight, and personalization-driven assistant that knows how you want specific analytics to be served.
In order to make that work, a number of moving parts need to come together as one well-oiled machine: Embedded interfaces (on-the-go via your device, in your email, chat, or in-app), pre-trained analytics services and training pipeline, the vehicle to facilitate the data model creation, and the right visualisation and narration to make the results digestible, trustable, and learning.
Could you elaborate more on data storytelling and its co-relation with BI?
Data storytelling forms a compelling narrative by putting data in context to show the challenges, insights and solutions of a specific business problem. It normally highlights a series of changes or trends over time through linked visualizations that combine to tell a story.
The difference between raw data and stories, the tipping point that will get users or listeners to take action, is the emotional engagement that is inspired by a compelling narrative. After a typical presentation, 5% of listeners remember the statistics and the concepts they described, but 63% will remember stories used to illustrate key concepts.
Dashboards and visualizations are integral parts of data storytelling, but they’re not the end of the story. A story should include a solid narrative and a context to successfully create your story.
Let’s talk about ‘X’ analytics & knowledge graphs. What’s your approach to “X analytics” at Sisense?
X analytics is an umbrella term coined by Gartner, which involves the analysis of all kinds of data, including written, text, social posts, images, historical papers, audio and more.
The world is wider than the traditional BI tabular data. It’s visual, it’s spoken, it’s audible, it’s written, it’s recorded. Why use just one of the senses and limit your perspective? By analyzing all of the available data out there, rather than just the tabulated, served up data, insights are deepened and predictions become more accurate.
Sisense recently used an ecosystem of ML service providers to help scan and surface the medical crowd wisdom of COVID treatments from piles of textual data from a site called G-Med.
There was no point in reinventing the wheel to build a video, image, speech, and text analysis tools — there are plenty of those on the market already.
The Sisense team pulled a data sample from the COVID-19 period out of G-Med’s database and imported it into the Sisense platform. The team then ran Amazon Comprehend on the information to determine the sentiment for each sentence. Sentiment is vital because things change rapidly in healthcare, and the COVID-19 pandemic advanced the pace of change to previously unknown levels.
Based on discussions about country-level differences in mortality rates after patients were given mechanical ventilation, Medin’Sight was able to present results to the community that were later proven to be accurate by scientific publications.
Tell us more about the “Knowledge Graphs” and their growing importance in 2020s.
Knowledge graphs will be the base of how all these huge data models and data stories are created, first as relatively stable creatures and, in the future, as on-demand, per each question.
Even though knowledge graphs have been around for a long time, only now do enterprise companies have the right infrastructure (both personnel and technology) to implement and analyze the insights these graphs hold.
Knowledge graphs recommend and expand queries, thereby improving the users experience, and allowing users to ask questions of their data in straightforward language and interact with it themselves. Knowledge graphs will build the connections and relationships of a company and will reveal insights you never knew were there.
Thank you, Ashley, for chatting with us! Have a productive New Year 2021.
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