Specialist
Executive at Microsoft Corp
Agenda
- Key trends in the database management industry and overall market environment, including 2023 demand outlook
- Market positionings of various players and convergence of offerings across AWS (NASDAQ: AMZN), Google Cloud Platform (GCP; NASDAQ: GOOGL), Snowflake (NYSE: SNOW), Databricks, MongoDB (NASDAQ: MDB), Oracle (NYSE: ORCL), IBM (NYSE: IBM) and others
- Current state of technology in the market, discussing common solution features, flexibility, integration capabilities and market reputation
- Recent trends and important innovation in AI such as OpenAI, image generation and impact of low-code application development on a broadening user base
- Go-to-market strategy, differentiation and pricing strategy for various offerings
Questions
1.
Could you highlight 2-3 key trends in how cloud database management vendors are leveraging AI for their database analytics solutions?
2.
A Gartner report stated that the AI software market will reach almost USD 134bn by 2025. Over the next five years, the market growth will accelerate from 14.4% CAGR in 2021 to 31.1% in 2025. Another report estimated a USD 209bn TAM by 2029. For such a large industry with so many use cases, how should we think about sizing the TAM, and do these estimates sound right to you?
3.
Are vendors doing a good job at gaining customer traction and expanding use cases? How much of a challenge is this proving to be and how are vendors approaching it for AI and database management software?
4.
Could you elaborate on which cloud database management vendors are doing the best job at expanding customer adoption?
5.
As the market has gotten tighter, we’ve seen some hyperscalers’ cloud usage dropping. How would you characterise the market’s growth for these solutions over the past year? Where were they hurting and which market areas continue to grow?
6.
You alluded to how AutoML [automated machine learning] and various low-code solutions are changing the market. How will these technologies continue to be adopted and disrupt the market, and how are cloud companies reacting?
7.
How is low-code development changing the market, especially looking at the analytics solutions provided by various cloud vendors?
8.
Do you see the standard data warehouse vendors such as Snowflake or Redshift leading the charge into more low-code solutions, or will it come from more data science-heavy machine learning lakehouse vendors such as Databricks or MongoDB?
9.
How long do you think it will take before low-code solutions become more ubiquitous for cloud analytics solutions?
10.
A Gartner study projected that 60% of all data used in the development of AI will be synthetic rather than real by 2024. Scale AI launched a synthetic data platform in 2022 called Scale Synthetic. Markets with traditionally inaccessible data such as healthcare are a clear opportunity to be able to use synthetic data. Can you comment on the market opportunity for synthetic data and how various vendors may be able to take advantage or be disrupted by this technology?
11.
How confident are you that synthetic AI solutions may become a part of product portfolios for cloud database analytics vendors such as Snowflake or Databricks? Could these offerings become significant drivers of their revenues and for increasing consumption?
12.
The Gartner report I cited said that 60% of data being used for development of AI would be synthetic by 2024. How long might it take for synthetic data to become a more viable investment opportunity for cloud database management analytics vendors? If not 2024, what date might it be?
13.
Large language models such as NLP [natural language processing] and GPT [Generative Pre-trained Transformer]-3 using Stable Diffusion have seen a lot of interest in 2022. One report states that nearly all of 2022’s largest AI financings were language start-ups such as Anthropic or Inflection. What are the current or near-term use cases for this technology in the industry, and how would you expect it to grow and disrupt the market? One use case that comes to mind is chatrooms or chatbots.
14.
Will the data lakehouse model, utilising a combination of relational and non-relational architecture, become more industry standard? How do you see cloud data warehousing solutions positioned vs non-relational database management vendors such as Databricks, MongoDB or Dremio?
15.
Do you see the companies we discussed as taking share from more entrenched players such as Snowflake or the hyperscalers, or might Snowflake and the hyperscalers be able to develop competing offerings that fortify their current market share?
16.
How would you rank Databricks, MongoDB, Dremio, Starburst and their solutions by how successful you think they will be in the market?
17.
Snowflake and Databricks recently developed a number of overlapping products such as Snowpark and SQL warehouse. Do you see these products eventually gaining traction for new logo acquisitions and leading to more direct competition between the two vendors, or do you think these products are more likely to remain positioned as add-on products for these companies’ current install bases?
18.
Snowflake’s forward-looking Q3 FY23 YoY revenue growth was at 67% for product revenue. The company lowered its guidance for Q4 FY23, given some macroeconomic headwinds, but moving into FY24, what would your expectations be for Snowflake and Databricks’ revenue growth rates? Do you think a recession will keep driving down that growth rate or might it bounce back?
19.
How important are cost of ownership and ROI evaluations when customers are evaluating vendors? What are the factors and evaluation criteria and how might these slow down growth for a more cost-conscious customer in a recession?
20.
You mentioned criteria such as time to market. Which vendors are executing this the best and which ones may be struggling?
21.
Which start-ups might be best-positioned to grow their market share?
22.
You highlighted many recent tech industry lay-offs. It seems some of these are happening at the more senior technologist level, whether in machine learning and AI groups or other cloud technologist-type roles. Are you seeing any particular companies snatching up this talent as they’re being laid off?
23.
How do you see BigQuery currently positioned in the market vs competitors? You said the platform has been very aggressive within its market, but it does have the smallest market share compared to other hyperscalers. Could you expand on its growth strategy and how you see it growing?
24.
What would you say are GCP’s [Google Cloud Platform’s] top 1-2 investment priorities?
25.
My understanding is there’s a view in the market that BigQuery is seen as a strong offering but with a smaller market share. What are your thoughts on comparing BigQuery with AWS, and how do you see competition playing out as BigQuery starts to gain market share?
26.
When evaluating BigQuery and AWS head-to-head, why would a customer choose one over the other and how is price factored in?
27.
Are any other vendors well-positioned for growth? On the start-up side or on the legacy side, there are companies such as Oracle with its OCI [Oracle Cloud Infrastructure] product.
28.
How successful do you think OCI will be? It seems the market is pretty split. Many people don’t have a lot of confidence in OCI, while others see the infrastructure Oracle brings as a big advantage. What’s your opinion?
29.
A lot of people in the market seem pretty negative on Cloudera’s outlook. However, in a recent Forum Interview [see Cloudera – Market Positioning & Trends in Big Data Analytics – 16 November 2022], we heard the hybrid split offering between on-prem and cloud as being a big point of differentiation. Would you agree the company still has a value with this?
30.
Do you have any comment on vendors such as Palantir and C3 AI entering the cloud market more aggressively? We’ve heard that verticalised out-of-the-box solutions is an area that will see a lot of growth, given so many customers cannot work with these solutions themselves.
31.
What’s your view on industry consolidation? Which industry segments do you see as most ripe for M&A, and do any particular companies come to mind that are either potential acquisition targets or acquirers that may have a product gap they’re looking to fill?
32.
How is R&D spend being impacted by AI, cloud analytics and the development of these next-generation technologies? Which of these technologies are most expensive to develop and how are companies managing these costs?
33.
How is responsible AI moving from a vague idea to becoming operationalised? Is there a market for these features yet? Putting aside conjecture about the distant future, how are corporate governance functions thinking about risks associated with AI today?
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