As we had reported in our July newsletter, the new Alternative Investment Funds Lawcame into force in July 27, 2014, appointing the Cyprus Securities and Exchange Commissioned(CySEC) as the competent supervisory authority for AIFs.
The new Law regulates the establishment and operation of AIFs in Cyprus and replaces the International Collective Investment Schemes Laws of 1999 and 2000.
In relation to the existing International Collective Investment Schemes (ICIS) that have been operating and supervised by the Central Bank of Cyprus or for any pending applications, the new AIF Lawprovided a period of transposition and supervision by new competent authority, CySEC.
More specifically, section 120(1) of the new Law, provides that ICIS that have been cleared to operate in accordance with the ICIS law previously applicable, may continue to operate:
- either as AIFs with limited number of persons, as long as they comply with the provisions of sections 114 – 118 of the AIF Law and submit to CySEC all the information and documents specified in section 120(1)(a).
- In this case they will continue to operate on the basis of the authorisation received by the Central Bank of Cyprus, without the requirement of receiving re-authorisation by CySEC;
- either as AIFs of Part II of the Law, as long as they are authorised by CySEC in accordance with section 13 of the AIF Law;
- either as AIFMs, as long as they are authorised by CySEC in accordance with section 8 of the Alternative Investment Fund Managers Law of 2013.
Initially, the above mentioned information and the applications for permission to operate under the AIF Law were required to be submitted toCySEC by November 25, 2014. Pursuant to an Announcement by CySEC last Friday, the period of transposition of existing operating ICIS into AIFs has been extended toMarch 25, 2014. The relevant amendment to AIF Law implementing this extension of time is due to be passed by the Cypriot Parliament within the next two weeks. In case of failure of the above mentioned procedure, the ICIS will be dissolved in accordance with the ICIS Laws. The new deadline for dissolution of ICIS has been extended to six months following March 25, 2014.
Business and data – building better operations
By Bryan Kirschner, Vice President Strategy, DataStax
Building your business on data. What have we learned so far?
Coming into 2020, running your business based on what your data told you was a reality for some businesses, and a goal for many more. The coronavirus pandemic forced all companies to become more digital and more data-driven.
What lessons have we learned so far, and how can companies improve their data-driven processes over time?
There’s a meme going round about how the C-level term that has had the most impact on business and IT strategy in the past few years is not the CEO or CIO, but COVID-19. For some, this will lead to a chuckle at most. For others, it will ring all too true. In 2020, running your business based on what your data told you was a reality for some businesses, and a goal for many more. The pandemic forced all companies to become more digital and more data-driven.
For all businesses, data will continue to be essential to their operations. According to Rita Sallam of Gartner, the top ten trends for data through the rest of 2020 will be about scaling up and being more agile with data, as “… data and analytics leaders require an ever-increasing velocity and scale of analysis in terms of processing and access to succeed in the face of unprecedented market shifts.” COVID-19 has made this trend inevitable.
What comes next for data?
All companies are therefore becoming ‘data-driven’ companies. The challenge coming up is therefore how data can keep being a differentiator for businesses when every enterprise has access to data and analytics.
While companies can all gather data and use it for their operations, the real differentiator is speed. It’s not just that companies can generate and store data at scale, it’s that they can make decisions faster and then deploy data in valuable ways more quickly than their competitors. This plays into the affirmative side of competing against other companies as well – when markets are healthy and dynamic, new opportunities can emerge that you can take advantage of by moving more quickly.
Asking the right questions to get the right answers
Keep a customer focus in mind for your approach to data. By asking questions that focus on what customers need, you can get a head start in creating value that customers are willing to pay for. For example, asking how much your customers are willing to pay for your data products can quickly show you where you are – either you have a great product in place already that data can improve, or it will show you where you need to work harder around using data effectively.
Similarly, you can use data around your goals to improve your decisions. Asking what data your customers want around their interactions with you, and how you can provide them with insights from that data, can get you started. Alongside this, you can look at more strategic goals like how you can improve your Net Promoter Score over and above your competitors, how to reduce churn, or increase lifetime value, by supplying your customers with data products.
The wrong questions about data focus on internal and political issues. For example, if your team has to answer questions on how to prove the net present value of data over five years, or how to negotiate data access between business units, then your focus is not on the customer. There are other questions that are reasonable to ask – for example, around security of data and justification for storage costs – but these can easily distract you from the opportunities that exist around that data. Discussing these questions can easily lead your teams down rabbit holes.
You will likely be better off solving exactly the governance and security questions you need to in order to deliver one specific, new data-driven experience to customers in the next quarter, and then the next quarter after that, in turn, versus trying to solve them in the abstract. Because those new experiences will themselves generate data, if you ship faster, you will learn faster. This is a new way of working for many, but getting this flywheel spinning is the key to staying ahead if you’re starting ahead–or stealing a march on competitors who don’t realize its importance.
What Skills Does a Data Scientist Need?
In this modern and complicated time of economy, Big data is nothing without the professionals who turn cutting-edge technology into actionable insights. These professionals are called Data Scientists. Modern businesses are awash with data and many organizations are opening up their doors to big data and unlocking its power that increases the value of data scientists. Data is one of the most important features of any organization which helps to make decisions based on facts, stats, and trends.
As the scope of data is growing, data science came up as a multidisciplinary field. Data science is an integral part of understanding the working of many industries, complex or intricate. It helps organizations and brands to understand their customers in a much better, enhanced, and empowered way. Data science can be helpful in finding insights for sectors like travel, healthcare, and education among others. Its importance is increased as it solves complex problems through Big Data. With data science, companies are using data in a comprehensive manner to target an audience by creating better brand connections. Nowadays data science is taking an important and big prime role in the growth process of brands, as it is opening new fields in terms of research and experiments.
Let us know about the much-hyped role of a data scientist, the skills required to become one, and the need to take data science training.
Who is a Data Scientist?
Data Scientists are the individuals who gather and analyze large sets of structured and unstructured data. It combines the roles of computer science, mathematics, and statistics to create actionable plans for companies and other organizations. They gather, analyze, and process the data and then find the filtered results. Their work is to make sense of large, messy, and unstructured data using sources such as social media, smart devices, digital channels, emails, etc.
In other words, data scientists are analytical data experts who solve complex problems through technical skills to explore what problems need to be solved with available data. They are struggling with data all the time and experimenting via complex mathematics and statistical analysis. Usually, data scientists are required to use advanced analytics technologies such as machine learning, advanced computing, and predictive modeling. They use various types of reporting tools and analytical skills to detect problems, patterns, trends, and connections between data sets. Their goal is to provide reliable information about campaigns and consumers that help companies to attract and engage their customers and grow the sales.
A job of a data scientist is also known and advertised as a machine learning architect or data strategy architect. Data scientists generally require enough educational and experiential background of big data platforms, tools including Hadoop, Pig, Hive, Spark, and MapReduce and programming languages such as SQL, Python, Scala, and Pearl; and computing languages like R.
Skills Needed To Become a Data Scientist
To become a data scientist, it is recommended to have a master’s degree. This means a very strong educational background and the deep knowledge is must-required to become a data scientist. You must have a bachelor’s degree in any stream such as computer science, Physical science, social science, statistics, and mathematics or engineering.
The skills required to become a data scientist are categorized into technical and non-technical. Some of them are mentioned below:
● R Programming
R is specially designed for data science to deal with big data. It is generally preferred for data science to gain in-depth knowledge of analytical tools. Almost 43% of data scientists are using R to solve data problems and statistical issues.
● Python Coding
The most required technical skill to become a data scientist is having the knowledge of the most common coding language that is Python along with C, C++, Java, and Pearl.
● Hadoop Platform
It is the second most important skill to be a data scientist. This platform is heavily used in several cases. Hadoop is used to convey the data quickly to different servers.
● Apache Spark
It is becoming the most popular big data technology in the whole world. Just like Hadoop, it is a big data computation framework, but it is faster.
● SQL Database/Coding
With SQL database and coding, data scientists are able to write and execute complex queries in SQL.
● Data Visualization
A data scientist can visualize the data with data visualization with tools such as ggplot, d3.js and Matplottlib, and Tableau.
● Machine Learning and AI
Machine learning techniques include reinforcement learning, neural networks, adversarial learnings, etc. Along with it, supervised machine learning, decision trees, logistic regression can help you stay ahead from other data scientists.
There are also some non-technical skills such as Intellectual curiosity, Communication skills, Business acumen, Teamwork, etc. that can make you a successful data scientist.
Ready to Learn Data Science?
Data Science is nowadays a buzzing word in the IT sector. It has become an evolutionary technology that everyone is talking about. Several people want to become data scientists. It is a versatile career that is used in many sectors such as health-care, banking, e-commerce industries, consultancy services, etc. This career is one of the most highly paid careers. Data science careers have been always in high demand so the seekers have numerous opportunities to start or boost their careers.
It is a widely abundant field and has vast career opportunities because there are very few people who have the required certifications and skill-set to become a complete data scientist. You can gain these skills by enrolling in an online data science training program. By learning from industry experts, you will have a strong foundation of data science concepts. You’ll also be able to work on different data science tools and industry projects through a training course. So it’s the right time to get certification and grab the golden opportunities in the Data Science career.
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How to use data to protect and power your business
By Dave Parker, Group Head of Data Governance, Arrow Global
Employees need to access data to do their jobs. But as data governance professionals, it’s our job to protect it. Therefore, we must perform a fine balancing act to weigh robust data protection against the productivity of workers who need the data to maintain business-as-usual working processes.
Data grows exponentially, and most organisations will admit that they simply don’t know what data they have, where it is, and the controls that exist around it. This creates 2 challenges:
- Burgeoning amounts of unstructured data makes the business increasingly vulnerable from external attackers or internal data breaches.
- Because data is the key to understanding a customer’s wants and needs, if the business can’t identify its data and unlock its value, it’s at a competitive disadvantage.
As a European investor and alternative asset manager, here at Arrow Global we take care of £50bn of assets and own a data estate exceeding 160TB. How we manage our data is key to our success. We understand the difficulties involved in opening up environments to allow people to work productively, while at the same time locking them down to protect our organisation.
When it comes to analytics, I believe that Arrow is highly proficient because we employ a talented team of data scientists. But even for us, the sheer volume of raw and processed data, that resides in both our structured systems and unstructured data repositories, has the potential to put our business at risk.
We know there’s always more that can be done to strengthen our security posture and ensure regulatory and contractual compliance, while at the same time using our data to drive the business forward.
Data protection isn’t just about compliance
For many organisations, data protection has centred on demonstrating compliance with the GDPR. At Arrow, our efforts have gone one step further to include our contractual exposure.
Being a more mature data organisation, we had previously tried to develop an application in-house to manage our data estate. However, with 160TB across the company in production data alone, we simply couldn’t achieve the scale we needed to handle the sheer volume of data. Of course, the volume is just the start – once you know what data you have, you then need to be able to categorise the data and put it into a structure, so the business can analyse it for a specific use case.
We knew we needed to go to market to find an industrial-strength data discovery product to replace our in-house application. By aligning our choice of product to our overall IT and change strategy, meant that ultimately, we ended up with a far better outcome than we’d anticipated.
Position data as both a risk and an asset
Data touches every part of an organisation, so when it came to building a business case for buying-in a data discovery software platform, we approached it in a way that would speak to different people at the same time. We did this by posing the question:
“What do we want to do with data in a way that is GDPR-compliant, contractually-compliant and enables us to better service our clients?”
These are the black and white tests of data governance – to recognise the importance of securing and protecting data. They’re applied in a way that enables us to commoditise data and use it to drive the business forward, by forcing us to consider how we would use the data – for example, creating value-based pricing for our clients.
In aligning the business case to initiatives that were already priorities within the boardroom, we knew that we’d gain the attention of the senior leadership team and it would be easier to get the buy-in and budget we needed. And in the end, everyone wins – we get what we need to protect the data, and the business gets to distil the data’s value to better meet our customers’ expectations.
Get visibility of data at scale
For us, things got really exciting once we were able to see all of our data at scale. We chose Exonar because it allowed us to discover our data in ways that other products couldn’t. And the interface between the user and Exonar meant that everyone – both technical and non-technical users – could understand the technology and the findings it revealed.
When we saw exactly what data was in the estate, where it was and who had access to it, data security became much easier and the risk of data being compromised was dramatically reduced. We can see exactly where the vulnerabilities are and restructure how our data is stored to strengthen security. Then over time, we can use search, workflow and analysis to optimise the infrastructure and continually identify new areas to improve.
Commercialise the data
From a wider-business perspective, once people can see the data, they can start asking “What if…” to query it and distil its value. But it’s more than just the data itself. It’s not uncommon for data relating to the same thing to exist in unconnected systems across the business. For example, customer interactions and incidents or events.
Exonar is capable of joining the dots in disparate data sets. By stitching these data sets together, we can get a better overall view of our customers and use the outcomes to think of new, different or better ways of serving them through enhancing or adapting our offerings.
Why other financial services businesses should also take a smarter approach to data
- By changing the way you approach data, you can use it to protect and power your business and the people you serve.
- By positioning data as both a risk and an asset, you elevate its position to give it priority in the boardroom. Ultimately, it’s data that helps the business make informed strategic decisions about how to strengthen its competitive advantage.
- By gaining visibility of data at scale, you can see exactly what data you have and where it is. This gives the business confidence about the actions needed to ensure it is secured in both a regulatory and contractually compliant way, and that people are doing the right thing with data at all times.
- And joining different data sets provides you with a single view of ‘X’ within your data, no matter where it is. Helping to support your wider-business strategy and priorities, it gives you the information you need to secure a business advantage and generate value.
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