Data governance has been on my mind a lot lately. With all the incessant talks and hype about Artificial Intelligence, the true value of AI comes from good data. Therefore, it is vital for any organization embarking on their AI journey to have good quality data. And the journey of the lifecycle of data in an organization starts at the point of ingestion, the data source of how data is either created, acquired to be presented up into the processing workflow and data pipelines for AI training and onwards to AI applications.
In biology, taxonomy is the scientific study and practice of naming, defining and classifying biological organisms based on shared characteristics.
And so, begins my argument of meshing these 3 topics together – data ingestion, data taxonomy and with Computational Storage. Here goes my storage punditry.
Data Taxonomy in post-injection
I see that data, any data, has to arrive at a repository first before they are given meaning, context, specifications. These requirements are different from file permissions, ownerships, ctime and atime timestamps, the content of the ingested data stream are made to fit into the mould of the repository the data is written to. Metadata about the content of the data gives the data meaning, context and most importantly, value as it is used within the data lifecycle. However, the metadata tagging, and preparing the data in the ETL (extract load transform) or the ELT (extract load transform) process are only applied post-ingestion. This data preparation phase, in which data is enriched with content metadata, tagging, taxonomy and classification, is expensive, in term of resources, time and currency.
Elements of a modern event-driven architecture including data ingestion (Credit: Qlik)
Even in the burgeoning times of open table formats (Apache Iceberg, HUDI, Deltalake, et al), open big data file formats (Avro, Parquet) and open data formats (CSV, XML, JSON et.al), the format specifications with added context and meanings are added in and augmented post-injection.
We often hear “Cyber Resilience” word thrown around these days. Every backup vendor has a cybersecurity play nowadays. Many have morphed into cyber resilience warrior vendors, and there is a great amount of validation in terms of Cyber Resilience in a data protection world. Don’t believe me?
Cyber Resilience, as well articulated in the video, is not old wine in a new bottle. The data protection landscape has changed significantly since the emergence of cyber threats and ransomware that it warrants the coining of the Cyber Resilience terminology.
But I want to talk about one very important cog in the data protection strategy, of which cyber resilience is part of. That is Immutability, because it is super important to always consider immutable backups as part of that strategy.
It is no longer 3-2-1 anymore, Toto.
When it comes to backup, I always start with 3-2-1 backup rule. 3 copies of the data; 2 different media; 1 offsite. This rule has been ingrained in me since the day I entered the industry over 3 decades ago. It is still the most important opening line for a data protection specialist or a solution architect. 3-2-1 is the table stakes.
Yet, over the years, the cybersecurity threat landscape has moved closer and closer to the data protection, backup and recovery realm. This is now a merged super-segment pangea called cyber resilience. With it, the conversation from the 3-2-1 backup rule in these last few years is now evolving into something like 3-2-1-1-0 backup rule, a modern take of the 3-2-1 backup rule. Let’s take a look at the 3-2-1-1-0 rule (simplified by me).
The 3-2-1-1-0 Backup rule (Credit: https://www.dataprise.com/services/disaster-recovery/baas/)
Last week, there was a press release by Qlik™, informing of a sponsored TechTarget®‘s Enterprise Strategy Group (ESG) about the state of responsible AI practices across industries. The study highlighted critical gaps in the approach to responsible AI, ethical AI practices and AI regulatory compliances. From the study, Qlik™ emphasizes on having a solid data foundation. To get to that bedrock foundation, we must trust the data and we must be responsible for the kinds of data that built that foundation. Hence, Data Trust and Data Responsibility.
There is an AI boom right now. Last year alone, the AI machine and its hype added in USD$2.4 trillion market cap to US tech companies. 5 months into 2024, AI is still supernova hot. And many are very much fixated to the infallible fables and tales of AI’s pompous splendour. It is this blind faith that I see many users and vendors alike sidestepping the realities of AI in the present state as it is.
AI is not always responsible. Then it begs the question, “Are we really working with a responsible set of AI applications and ecosystems“?
I am on a learning streak again. The most prominent technology that keeps landing on my tray at present is, of course, Artificial Intelligence (AI).
AI is hot. Very hot. And overhyped. Everyone is an expert nowadays. Yeah, right. Not me.
Underneath that glossy veneer of the AI hype, there are much going on behind the scenes to make AI great. The 2 areas I have been involved in and practiced for a long time are data infrastructure a.k.a. storage, and data management. And both are playing prominent parts in the advancement of the AI ecosystem. This makes me very excited.
I am no expert, but learning from various sources is already telling me that AI is pushing both storage and data management harder than ever before, much harder than traditional enterprise on-premises use cases and even the cloud computing applications. I ask myself, “where do I start my learning again?” as I journal my process.
Storage performance in a Data Pipeline
Speed of how AI responds is Trust. The faster it is to the accurate and relevant responses will build trust in AI. To get to the speed that we want is not an easy thing, and storage a.k.a. data infrastructure is doing its part. I pick up my learning from understanding the AI pipeline. One early help comes from my friend, Gina Rosenthal, who attended the Solidigm‘s presentation at AI Field Day in February 2024. Her article, titled “Why storage matters for AI – Solidigm“, kickstarted my learning juices again.
I was particularly captured by this slide in Gina’s article. It defines the laborious path data takes to become useful for AI applications.
Storage and the 5 stages of AI Work (reference: Solidigm presentation on AI Field Day)
In the past weekend, I watched a CNA Insider video delving into Data Theft in Malaysia. It is titled “Data Theft in Malaysia: How your personal information may be exploited | Cyber Scammed”.
You can watch the 45-minute video below.
Such dire news is nothing new. We Malaysians are numbed to those telemarketers calling and messaging to offer their credit card services, loans, health spa services. You name it; there is something to sell. Of course, these “services” are mostly innocuous, but in recent years, the forms of scams are risen up several notches and severity levels. The levels of sophistication, the impacts, and the damages (counting financial and human casualties) have rocketed exponentially. Along with the news, mainstream and others, the levels of awareness and interests in data, especially PII (personal identifiable information) in Malaysians, are at its highest yet.
Somewhere, there is a misconception that data processing is cheap. That stems from the well-known pricings of the capacities of public cloud storage that are a fraction of cents per month. But data in storage has to be worked upon, and has to be built up and protected to increase its value. Data has to be processed, moved, shared, and used by applications. Data induce workloads. Nobody keeps data stored forever and never be used again. Nobody buys storage just for capacity alone.
We have a great saying in the industry. No matter, where the data moves, it will land in a storage. So, it is clear that data does not exist in ether. And yet, I often see how little attention and prudence and care, when it comes to data infrastructure and data management technologies, the very components that are foundational to great data.
Great data management for Great AI
AI is driving up costs in data processing
A few recent articles drew my focus into the cost of data processing.
I was listening to several storage luminaries in the GestaltIT’s podcast “No one understands Storage anymore” a few of weeks ago. Around the minute of 11.09 in the podcast, Dr. J. Metz, SNIA® Chair, brought up this is powerful quote “Storage does not mean Capacity“. It struck me, not in a funny way. It is what it is, and it something I wanted to say to many who do not understand the storage solutions they are purchasing. It exemplifies what is wrong in the many organizations today in their understanding of investing in a storage infrastructure project.
This is my pet peeve. The first words uttered in most, if not all storage requirements in my line of work are, “I want this many Terabytes of storage“. There are no other details and context of what the other requirement factors are, such as availability, performance, future growth, etc. Or even the goals to achieve when purchasing a storage system and operating it. What is the improvement they are looking for?What are the problems to solve?
Where is the OKR?
It pains me to say this. For the folks who have in the IT industry for years, both end users and IT purveyors alike, most are absolutely clueless about OKR (Objectives and Key Results) for their storage infrastructure project. Many cannot frame the data challenges they are facing, and they have no idea where to go next. There is no alignment. There is no strategy. Even worse, there is no concept of how their storage infrastructure investments will improve their business and operations.
Just the other day, one company director from a renown IT integrator here in Malaysia came calling. He has been in the IT industry since 1989 (I checked his Linkedin profile), asking to for a 100TB storage quote. I asked a few questions about availability, performance, scalability; the usual questions a regular IT guy would ask. He has no idea, and instead of telling me he didn’t know, he gave me a runaround of this and that. Plenty of yada, yada nonsense.
In the end, I told him to buy a consumer grade storage appliance from Taiwan. I will just let him make a fool of himself in front of his customer since he didn’t want to take accountability of ensuring his customer get a proper enterprise storage solution in good faith. His customer is probably in the same mould as well.
Defensive Strategies as Data Foundations
A strong storage infrastructure foundation is vital for good Data Credibility. If you do the right things for your data, there is Data Value, and it will serve your business well. Both Data Credibility and Data Value create confidence. And Confidence equates Trust.
In order to create the defensive strategies let’s look at storage Availability, Protection, Accessibility, Management Security and Compliance. These are 6 of the 8 data points of the A.P.P.A.R.M.S.C. framework.
Offensive Strategies as Competitive Advantage
Once we have achieved stability of the storage infrastructure foundation, then we can turn over and drive towards storage Performance, Recovery, plus things like Scalability and Agility.
With a strong data infrastructure foundation, the organization can embark on the offensive, and begin their business transformation journey, knowing that their data is well run, protection, and performs.
Alignment with Data and Business Goals
Why are the defensive and offensive strategies requiring alignment to business goals?
The fact is simple. It is about improving the business and operations, and setting OKRs is key to measure the ROI (return of investment) of getting the storage systems and the solutions in place. It is about switching the cost-fearing (negative) mindset to a profit-conviction (positive) mindset.
For example, maybe the availability of the data to the business is poor. Maybe there is the need to have access to the data 24×7, because the business is going online. The simple measurable fact is we can move availability from 95% uptime to 99.99% uptime with an HA storage system.
Perhaps there are concerns about recoverability in the deluge of ransomware threats. Setting new RPO goals from 24 hours to 4 hours is a measurable objective to enhance data resiliency.
Or getting the storage systems to deliver higher performance from 350 IOPS to 5000 IOPS for the database.
What I am saying here is these data points are measurable, and they can serve as checkpoints for business and operational improvements. From a management perspective, these can be used as KPI (key performance index) to define continuous improvement of Data Confidence.
Furthermore, it is easy when a OKR dashboard is used to map the improvement markers when organizations use storage to move from point A to point B, where B equates to a new success milestone. The alignment sets the paths to the business targets.
Storage does not mean only Capacity
The sad part is what the OKRs and the measured goals alignments are glaringly missing in the minds of many organizations purchasing a storage infrastructure and data management solution. The people tasked to source a storage technology solution are not placing a set of goals and objectives. Capacity appears to be the only thing on their mind.
I am about to meet a procurement officer of a customer soon. She asked me this question “Why is your storage so expensive?” over email. I want to change her mindset, just like the many officers and C-levels who hold the purse strings.
Let’s frame the use storage infrastructure in the real world. Nobody buys a storage system just to keep data in there much like a puddle keeps stagnant water. Sooner or later the value of the data in the storage evaporates or the value becomes dull if the data is not used well in any ways, shape or form.
Storage systems and the interconnected pathways from on premises, to the next premises, to the edge and to the clouds serve the greater good for Data. Data is used, shared, shaped, improved, enhanced, protected, moved, and more to deliver Value to the Business.
Storage capacity is just one of the few factors to consider when investing in a storage infrastructure solution. In fact, capacity is probably the least important piece when considering a storage solution to achieve the company’s OKRs. If we think about it deeper, setting the foundation for Data in the defensive manner will help elevate value of the data to be promoted with the offensive strategies to gain the competitive advantage.
Storage infrastructure and storage solutions along with data management platforms may appear to be a cost to the annual budgets. If you know set the OKRs, define A to get to B, alignment the goals, storage infrastructure and the data management platforms and practices are investments that are worth their weight in gold. That is my guarantee.
On the flip side, ignoring and avoiding OKRs, and set the strategies without prudence will yield its comeuppance. Technical debts will prevail.
There was a Super Blue Moon a few days ago. It was a rare sky show. Friends of mine who are photo and moon gazing enthusiasts were showing off their digital captures online. One ignorant friend, who was probably a bit envious of the other people’s attention, quipped that his Oppo Reno 10 Pro Plus can take better pictures. Oppo Reno 10 Pro Plus claims 3x optical zoom and 120x digital zoom. Yes, 120 times!
Yesterday, a WIRED article came out titled “How Much Detail of the Moon Can Your Smartphone Really Capture?” It was a very technical article. I thought the author did an excellent job explaining the physics behind his notes. But I also found the article funny, flippant even, when I juxtaposed this WIRED article to what my envious friend was saying the other day about his phone’s camera.
Super Blue Moon 2023
Open Source storage expectations and outcomes
I work for iXsystems™. Open Source has been its DNA for over 30 years. Similarly, I have also worked on Open Source (decades before it was called open source) in my home labs ever since I entered the industry. I had SoftLanding Linux System 3.5″ diskette (Linux kernel 0.99), and I bought a boxed set of FreeBSD OS from Walnut Creek (photo below). My motivation was to learn as much as possible about information technology world because I was making my first steps into building my career (I was also quietly trying to prove my father wrong) in the IT industry.
FreeBSD Boxed Set (circa 1993)
Open source has democratized technology. It has placed the power of very innovative technology into the hands of the common people With Open Source, I see the IT landscape changing as well, especially for home labers like myself in the early years. Social media platforms, FAANG (Facebook, Apple, Amazon, Netflix, Google), etc, etc, have amplified that power (to the people). But with that great power, comes great responsibility. And some users with little technology background start to have hallucinated expectations and outcomes. Just like my friend with the “powerful” Oppo phone.
Likewise, in my world, I have plenty of anecdotes of these types of open source storage users having wild expectations, but little skills to exact the reality.
On the road, seat belt saves lives. So does the motorcycle helmet. But these 2 technologies alone are probably not well received and well applied daily unless there is a strong ecosystem and culture about road safety. For decades, there have been constant and unrelenting efforts to enforce the habits of putting on the seat belt or the helmet. Statistics have shown they reduce road fatalities, but like I said, it is the safety culture that made all this happen.
On the digital front, the ransomware threats are unabated. In fact, despite organizations (and individuals), both large and small, being more aware of cyber-hygiene practices more than ever, the magnitude of ransomware attacks has multiplied. Threat actors still see weaknesses and gaps, and vulnerabilities in the digital realms, and thus, these are lucrative ventures that compliment the endeavours.
Time to look at Data Management
The Cost-Benefits-Risks Conundrum of Data Management
And I have said this before in the past. At a recent speaking engagement, I brought it up again. I said that ransomware is not a cybersecurity problem. Ransomware is a data management problem. I got blank stares from the crowd.
I get it. It is hard to convince people and companies to embrace a better data management culture. I think about the Cost-Benefits-Risk triangle while I was analyzing the lack of data management culture used in many organizations when combating ransomware.
I get it that Cybersecurity is big business. Even many of the storage guys I know wanted to jump into the cybersecurity bandwagon. Many of the data protection vendors are already mashing their solutions with a cybersecurity twist. That is where the opportunities are, and where the cool kids hang out. I get it.
Cybersecurity technologies are more tangible than data management. I get it when the C-suites like to show off shiny new cybersecurity “toys” because they are allowed to brag. Oh, my company has just implemented security brand XXX, and it’s so cool! They can’t be telling their golf buddies that they have a new data management culture, can they? What’s that?
Ho hum. Another day, and another data leak. What else is new?
The latest hullabaloo in my radar was from one of Malaysia’s reverent universities, UiTM, which reported a data leak of 11,891 student applicants’ private details including MyKad (national identity card) numbers of each individual. Reading from the news article, one can deduced that the unsecured link mentioned was probably from a cloud storage service, i.e. file synchronization software such as OneDrive, Google Drive, Dropbox, etc. Those files that can be easily shared via an HTTP/S URL link. Ah, convenience over the data security best practices.
Cloud File Sync software
It irks me when data security practices are poorly practised. And it is likely that there is ignorance of data security practices in the first place.
It also irks me when many end users everywhere I have encountered tell me their file synchronization software is backup. That is just a very poor excuse of a data protection strategy, if any, especially in enterprise and cloud environments. Convenience, set-and-forget mentality. Out of sight. Out of mind. Right?
Convenience is not data security. File Sync is NOT Backup
Many users are used to the convenience of file synchronization. The proliferation of cloud storage services with free Gigabytes here and there have created an IT segment based on BYOD, which transformed into EFSS, and now CCP. The buzzword salad involves the Bring-Your-Own-Device, which evolved into Enterprise-File-Sync-&-Share, and in these later years, Content-Collaboration-Platform.
All these are fine and good. The data industry is growing up, and many are leveraging the power of file synchronization technologies, be it on on-premises and from cloud storage services. Organizations, large and small, are able to use these file synchronization platforms to enhance their businesses and digitally transforming their operational efficiencies and practices. But what is sorely missing in embracing the convenience and simplicity is the much ignored cybersecurity housekeeping practices that should be keeping our files and data safe.
