Inception Data

[Crystle Rike]

Inissuing both the 2013 Letter and the 2019 Letter, FINRA acknowledged that PIP data is often used to model the performance of an index prior to the inception date of a product in order to illustrate how the index would have performed under historical market conditions. The 2019 Letter is a further acknowledgment by FINRA that PIP data can be a valuable method for Institutional Investors (as defined below) [4] that understand the limitations of PIP data to examine historical index performance and levels the playing field between member firms distributing passively managed Open-end Funds and member firms distributing passively managed ETPs.

Rule 2210(d) states that no member firm may make any false, exaggerated, unwarranted, or misleading statements in any communication with the public and may not publish, circulate, or distribute any public communication that the member knows or has reason to know contains any untrue statement of a material fact or is otherwise false or misleading. FINRA has historically interpreted Rule 2210(d) to prohibit the inclusion of any hypothetical back-tested performance data (including PIP data) in communications with the public by a member firm believing it to be inherently false and misleading in violation of FINRA Rule 2210(d)(1)(B). [5] FINRA has expressed concerns that back-tested performance invariably depicts favorable results, because it benefits from hindsight and components could be selectively used for inclusion in the track record with the knowledge of their performance.

In recognizing the historical overly broad application of FINRA Rule 2210(d) to Institutional Communications, FINRA has further relaxed its position with respect to hypothetical back-tested data to permit PIP data under the conditions set forth in the 2019 Letter. Member firms distributing passively managed Open-end Funds will now be able to provide PIP data with respect to such funds to intermediaries and other Institutional Investors in much the same manner as member firms distributing and marketing passively managed ETPs have been able to since the issuance of the 2013 Letter. To be consistent with the guidance in the 2019 Letter, in addition to complying with all other applicable FINRA rules and federal securities laws, Institutional Communications relating to Open-end Funds containing PIP data are required to comply with the following conditions:

The 2019 Letter is an important shift in FINRA staff views on PIP data in Institutional Communications that acknowledges that passively managed Open-end Funds should be treated in a similar manner as passively managed ETPs and will be a welcome development for member firms wishing to include PIP data in marketing materials for the passively managed Open-end Funds they distribute.

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Hello @AastaLLL ,

I have trained ssd inception v2 model using my dataset.

The detection is good in tensorflow.

then I converted to uff and tensorrt.

the detections are good there too (tensorRT python version)

I have certain measures stored in the database table that are all inception to date financials at the quarter end. To represent these financials properly, I need to pick the amounts from the quarter month end i.e. Mar, Jun, Sep and Dec.

Assuming there is a Calendar Table with Year and Quarter columns, create a relationship from the Date column of your Data Table to the Date column of the Calendar Table. To your visual/slicers, drag Year and Quarter from the Calendar Table and select a Year and Month. Write these measures

So, if a user selects the Quarter Month as 2020Q1, then the Debit and Debit_Sub should just pull in the value as of quarter month end which is Mar 2020. Right now, it is summing up the amount for Jan 2020, Feb 2020 and Mar 2020 which is not correct.

I just had another follow-up question on the report layout. Other than, Debit and Debit_Sub, I have other measures as well as part of my report, but those are not stored Inception to Date, so they match correctly when Power BI sums up by default.

Should I rever the Unpivot back to the way it was done originally i.e. continue to have the measures shown as separate columns in the table visualization? Is there an alternate way to represent the data in the unpivoted form currently.

Since we created specific measures catering to the financials that are stored As-At or Inception to Date like Debit and Debit_Sub, (which are part of Type alongwith other measures), it will not show the correct value in the table since our measures created for Debit and Debi_Sub are separate and not part of Type.

My question is - In the current setup, since we have multiple measures stored under Type, some of which are Inception to Date and rest are not Inception to Date and can be summed up, I was thinking the best way would be to leave the measures as separate columns in the dataset and bring them into the table accordingly. Not sure if there is any other solution for this.

To get the best of the time intelligence function. Make sure you have a date calendar and it has been marked as the date in model view. Also, join it with the date column of your fact/s. Refer :

-calendar-table-in-power-bi-using-dax-functions

-date-table-power-bi

-a-simple-date-table-in-dax/

I was never a Data Engineer nor a Data Scientist. In fact, I was a senior developer, although I have not been coding for a long time (nowadays I code markUp text for books and articles, a few excel macros and I keep a WordPress website; but this should not count). Despite not coding anymore, I have always been following up with the engineering practices for digital product development.

I really enjoy digital product development. In the beginning of my career, I used to look at the product from a technical perspective. As I progressed in my career (and my facilitation skills), I realised I should look at a digital product from three different angles: technology, User eXperience and business.

People and process. This is exactly where I focused my professional life! I started as a developer, then senior developer, then I changed my role to Agile coach; nowadays I say I am an expert inception facilitator. I live and breathe how to bring people together around a combination of agile processes and methodologies to deliver great products. In the last decade, I specialised myself on the beginning of agile teams, projects, and products: inception (or similar workshops).

Lean Inception is highly influenced by Lean StartUp and the concept of Minimum Viable Product (MVP). When experimenting with this new inception style, I deviated from traditional inceptions that were typically covering the overall project understanding, slicing, estimating, and planning.

Data Mesh focuses on organisational change. Lean Inception is a method (one amongst many others) that helps drive organisational changes. Lean Inception provides agile teams with a good level of autonomy while aligned with the desired business outcomes.

Lean inception activities are flexible enough so they can work for a product team within an organisation that has nothing to do with Data Mesh, as well as for teams actively involved in a Data Mesh transformation. Typically, teams that deliver analytical use cases and build Data products, and enhance the data platform.

Data Mesh suggests a thin slicing approach for delivering value faster via Use Cases. Similarly, Lean Inception aims to align people about the MVP and the increments (thin slices). Both, Data Mesh and Lean Inception, advocate for incremental value delivery and fast validation of business outcomes.

The challenges for a Data Mesh transformation are huge. For example, the need to evolve the mesh along with the self-serve data platform. These are also common challenges faced on many Lean Inceptions. For example: inceptions for a product versus an inception for a platform. How to facilitate it? What should be the MVP for a platform team? I cover some of these thoughts in this article: Team topologies and MVP.

Lean Inception is a crucial agile methodology for aligning teams on effective product creation. Introduced by Paulo Caroli, it combines Design Thinking and Lean StartUp techniques to define strategies and Minimum Viable Product (MVP) scope. It is valuable for large projects, startups, and business innovations. Not suitable for discovery activities, prototyping decisions, or cross-team alignment. Active participants, stakeholders, and skilled facilitators are essential for the success of this collaborative process. Lean Inception is fundamental for guiding teams toward meaningful and efficient product outcomes.

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